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Author Information: Gregory Sandstrom, Arena Blockchain, gregory.sandstrom@gmail.com.

Sandstrom, Gregory. “Is Blockchain an ‘Evolutionary’ or ‘Revolutionary’ Technology, and So What If It Is?: Digitally Extending Satoshi Nakamoto’s Distributed Ledger Innovation.” Social Epistemology Review and Reply Collective 8, no. 3 (2019): 17-49.

The pdf of the article gives specific page references, and includes the full text of the article. Shortlink, Part One: https://wp.me/p1Bfg0-47f. Shortlink: Part Two: https://wp.me/p1Bfg0-47m

Image by Kevin Krejci via Flickr / Creative Commons

 

“If you cry ‘Forward!’ you must without fail make plain in what direction to go. Don’t you see that if, without doing so, you call out the word to both a monk and a revolutionary they will go in directions precisely opposite?” – Anton Chekhov

“I’m better with code than with words though.” – Satoshi Nakamoto[1]

Did Satoshi Nakamoto, the pseudonymous creator of Bitcoin, actually invent anything new that had not previously existed before? Should people stop referring to a ‘blockchain revolution’ and instead call blockchain a ‘technological evolution’ that happened gradually and was caused randomly by environmental pressures rather than the intentional acts of a unique inventor? These basic questions make up the core of this paper, along with the suggestion that an alternative way of describing blockchain development makes considerably more sense than using the concept of ‘evolution’ in the digital era.

While it is unoriginal to ask whether blockchain distributed ledger technology should be thought of as an ‘evolution’ or a ‘revolution,’ since many people have asked it already (see bibliography below, including texts and videos), in this paper I’ll go a step deeper by looking at what people actually mean when they refer to blockchain as either an ‘evolution’ or a ‘revolution,’ or rather inconsistently as both at the same time.

In short, I’ll distinguish between their colloquial, ideological and technical uses and ask if one, both or neither of these terms is accurate of the changes blockchain has made, is making and will make as a new global digital technology.

Introduction: From the Book of Satoshi

In the Foreword to The Book of Satoshi: The Collected Writings of Bitcoin Creator Satoshi Nakamoto, libertarian Bitcoin activist Jeff Berwick wrote: “Bitcoin has changed everything. Its importance as an evolution in money and banking cannot be overstated. Notice I don’t use the word ‘revolution’ here because I consider Bitcoin to be a complete ‘evolution’ from the anachronistic money and banking systems that humanity has been using—and been forced by government dictate to use—for at least the last hundred years.” (2014: xvii)

While I don’t really understand what he means by a ‘complete evolution,’ Berwick’s attention to the difference in meaning between ‘evolution’ and ‘revolution’ regarding Bitcoin nevertheless sets the stage for this exploration of blockchain technology, as we consider its current development trajectory. Which term is more suitable?

Worth noting, nowhere in Satoshi Nakamoto’s collected writings is either the term ‘evolution’ or ‘revolution’ to be found. Berwick’s interpretation of ‘blockchain evolution,’ framed within his worldview as an anarcho-capitalist, is thus of his own making and not one that derives from Nakamoto himself. I’ll touch on why I believe that is below. Also of note, the book’s writer and compiler of Nakamoto’s writings, Phil Champagne, states that, “Bitcoin, both a virtual currency and a payment system, represents a revolutionary concept whose significance quickly becomes apparent with a first transaction. … Bitcoin has therefore clearly sparked a new technological revolution that capitalizes on the Internet, another innovation that changed the world.” (2014: 2, 7)

Champagne closes the book stating, “Satoshi Nakamoto brought together many existing mathematical and software concepts to create Bitcoin. Since then, Bitcoin has been an ongoing experiment, continuing to evolve and be updated on a regular basis. It has, so far, proven its utility and revolutionized the financial and monetary industry, particularly the electronic payment system, and is being accepted worldwide.” (2014: 347) The use of both ‘evolution’ and ‘revolution’ in past and present tense shows a debate exists even within this one book about which term best fits blockchain’s current and future status in society.

This paper will look closely at the difference between these two terms as they relate to blockchain, largely staying away from speculation about cryptocurrencies, i.e. digital tokens, crypto-assets, and/or crypto-securities. It will primarily serve to catalogue the way people have used these two terms with respect to blockchain and cryptocurrency and ask if they are suitable or unsuitable terms. In conclusion, I offer an analysis of why the distinction between these two terms matters as different ways to describe change-over-time and assess an alternative model to analyse and discuss these changes called ‘digital extension services.’ 

Reflexive Background and Context 

To set the background and context, let me write reflexively about why I am writing this paper. Over the past 15+ years studying the topic, I’ve become somewhat of an expert on how the term ‘evolution’ is used outside of the natural-physical sciences, in theories such as ‘social and cultural evolution,’ ‘evolutionary economics’ and ‘technological evolution.’

I wrote a master’s thesis comparing the concepts of ‘evolution,’ ‘extension’ and ‘Intelligent Design,’ and have published more than 20 papers and delivered more than 30 presentations at international conferences outlining and exploring the limits of ‘evolutionary’ thinking as well as promoting the notion of ‘human extension’ in social sciences and humanities[2].

My interest in this paper is to clear up what appears as massive public confusion and oftentimes puzzling equivocation about various types of change-over-time, especially non-evolutionary changes such as revolution, development, emergence, and extension. Some people think there is no such thing as a ‘non-evolutionary’ change since all change must be ‘evolutionary,’ in response to which I would like to set the record straight.

There are undoubtedly some people who will consider this paper and having written it to be a complete waste of time and for them, it’s best to stop reading at the end of this sentence. However, others may find in this exploration a key distinction towards gaining even a small bit of insight and perhaps some understanding into the considerable differences between biological change-over-time and technological development[3], innovation and planning, the latter which generally fall outside of the meaning of ‘evolution.’

Notably, I find it somewhat humorous for having studied this rather arcane social epistemological topic quite closely for many years to be able to write this paper now. It’s meant that I’ve had to lock horns repeatedly with ideological (young earth) creationists, Intelligent Design advocates and evolutionists on many occasions along the way[4]. What I have discovered is that sometimes choosing the right term matters and sometimes it doesn’t; some people want to use a term to mean whatever they want it to mean[5] and it’s most often not worth taking the time in trying to stop or persuade them.

When I learned in 2016 that blockchain technology is about more than just cryptocurrency, and that it also has potentially significant and far-reaching implications for a variety of social, cultural and educational uses, it simply made sense to bring some of the knowledge I had gathered as an associate professor and researcher into my study of distributed ledgers, which is what leads to this text.

In Q3 2017, I asked and answered myself on Twitter as follows: “Is blockchain really evolving of its own accord? No.” I copied that message to the Managing Director of the Blockchain Research Institute (BRI) in Toronto, Hilary Carter, who I had met that summer at the Blockchain Government Forum in Ottawa. She replied: “Agreed! Evolution is a series of beneficial genetic accidents. Blockchain and the development of the community is entirely intentional.” (24 Sep 2017) That exchange happened after I had recently arrived in Yangon, Myanmar, first to teach, then to work as Director of Blockchain Innovation at an educational technology startup company. I had many new things and needs to focus on and didn’t think about it too much further at that time.

However, after returning to Canada in 2018, I later raised this topic again directly in conversation with Carter[6]. While she still stands behind the view that blockchain is indeed a revolutionary phenomenon and that its development is based upon the various intentions of its builders and creators, she also suggested that, “the blockchain ecosystem is [an] evolution,” that it is in a state of maturation, and that, “no one is controlling it.” It is the latter contention that I’d like to take up again now and ‘unpack’ during the course of this paper.

Carter’s view, to which I will return below, raises an important question about how blockchain was invented, as well as the way that blockchain ecosystem development is currently being planned and executed, and both how and why people are aiming for social scalability and public adoption. Also, it raises the question of what then counts as the ‘blockchain revolution’ that BRI founder Don Tapscott wrote a book about with his son Alex in 2016.

To me, Carter’s original comment that blockchain development is ‘entirely intentional’ is obviously correct and requires no further commentary for validation. However, it also signifies that there is at least some type of ‘control’ when it comes to actual blockchain technology building, even if the trajectory of distributed ledgers aren’t being controlled, nor are they entirely predictable, by any single person or company, anywhere in the world.

My prior research in sociology of science had shown that while the term ‘evolution’ is used by not a few people in a basic colloquial sense simply as a synonym for ‘change,’ it can also be used, and not rarely, in an ideological sense that draws on ‘cultural evolutionary’ theories in SSH or in the case of technology, one that adheres to the so-called ‘laws of software evolution[7]‘ (M. Lehman). It is the latter usage of the term ‘evolution’ that I wholeheartedly reject and think has caused great damage to human self-understanding and initiative.

Let it be clear, however, in stating this that I am not one of the ‘new evolution deniers’ (Wright 2018) pursuing an anti-biology or anti-science blank slate ideology that doesn’t acknowledge change-over-time, which is evident in many ways across a range of cultural issues. Rather, I’m a dedicated social scientific researcher and more recently community builder of blockchain technology who rejects the notion that ‘no one is in control’ of what is being developed (i.e. ‘unguided evolution’).

Likewise, I strongly reject the misanthropic worldview that claims ‘there is no purpose[8]‘ (Dawkins) in change-over-time. I oppose both of these positions as dehumanising. So, with this context provided, the following sections present my research findings into how other people use the terms ‘evolution’ and ‘revolution’ with respect to blockchain technology.

Equivocating Between Evolution and Revolution 

“Bitcoin is a completely new narrative. It alters everything, and in 20 to 30 years from now, people will not recognise the world we are in because of Bitcoin.” – Craig Steven Wright (2019a)

Many writers on the topic of blockchain switch back and forth equivocally between ‘evolution’ and ‘revolution,’ apparently without much rhyme or reason, not carefully distinguishing between them. Rather curiously, this includes the Tapscotts. “We strongly believe that India has the potential to lead the blockchain revolution[9],” said Don Tapscott in 2018.

And there are indeed many places where Don and his son Alex use the term ‘revolution’ to describe blockchain in their 2016 book, which I will outline in the following paragraphs. They write, “Like the first generation of the Internet, the Blockchain Revolution promises to upend business models and transform industries. But that is just the start. Blockchain technology is pushing us inexorably into a new era, predicated on openness, merit, decentralization, and global participation.” (Ibid) This type of language continues throughout the book, which explains why they gave it the title they did.

However, they also use the term ‘evolution’ to describe technological change. “The Web is critical to the future of the digital world,” they say, “and all of us should support efforts under way to defend it, such as those of the World Wide Web Foundation, who are fighting to keep it open, neutral, and constantly evolving.” (Ibid)

They quote Blake Masters, who states, “Bear in mind that financial services infrastructures have not evolved in decades. The front end has evolved but not the back end. … posttrade infrastructure hasn’t really evolved at all.” (Ibid) Likewise, they cite Joseph Lubin, who says:

“I am not concerned about machine intelligence. We will evolve with it and for a long time it will be in the service of, or an aspect of, Homo sapiens cybernetica. It may evolve beyond us but that is fine. If so, it will occupy a different ecological niche. It will operate at different speeds and different relevant time scales. In that context, artificial intelligence will not distinguish between humans, a rock, or a geological process. We evolved past lots of species, many of which are doing fine (in their present forms).” (Ibid)

The Tapscotts in this vein also consider human-made technology itself, not just biology, as an ‘evolutionary’ phenomenon. They thus label one of their chapters, “The Evolution of Computing: from mainframes to smart pills.” (Ibid) “Unlike our energy grid,” they say, “computing power has evolved through several paradigms. In the 1950s and 1960s, mainframes ruled—International Business Machines and the Wild ‘BUNCH’ (Burroughs, Univac, National Cash Register Corp., Control Data, and Honeywell).

In the 1970s and 1980s, minicomputers exploded onto the scene.” (Ibid) They continue this line of thinking, suggesting that, “Driven by the same technological advances, communications networks evolved, too. From the early 1970s, the Internet (originating in the U.S. Advanced Research Projects Agency Network) was evolving into its present-day, worldwide, distributed network that connects more than 3.2 billion people, businesses, governments, and other institutions. The computing and networking technologies then converged in mobile tablets and handhelds. BlackBerry commercialized the smart phone in the early aughts, and Apple popularized it in the iPhone in 2007.” (Ibid)

Yet at some point unstated, they switch back to ‘revolutionary’ language, suggesting that, “We’re beginning the next major phase of the digital revolution.” (Ibid) They cite Michelle Tinsley of Intel, who “explained why her company is deeply investigating the blockchain revolution: “When PCs became pervasive, the productivity rates went through the roof. We connected those PCs to a server, a data center, or the cloud, making it really cheap and easy for lean start-ups to get computer power at their fingertips, and we’re again seeing rapid innovation, new business models.”

Just imagine the potential of applying these capabilities across many types of businesses, many untouched by the Internet revolution.” (Ibid) In short, their view is that “the technology is always evolving and designs are ever improving.” (Ibid) This encapsulates their equivocating meaning of ‘blockchain revolution,’ from one of the most widely cited texts in the field of blockchain technology.

Carter followed up with me after receiving the first draft of this paper to clarify her position. She explains, “We’ve evolved from single-purpose peer to peer electronic cash to Ethereum to private distributed ledgers to Cryptokitties. Everything is intentional. Evolution post-Bitcoin is more a figure of speech to reflect that blockchain systems have changed[10].” She continues, saying that, “Blockchain was no accidental software that emerged from the first generation of the internet.”

This sentence brings in another ‘change-over-time’ term with the notion of ’emergence,’ that adds to the linguistic feature of this analysis. Carter concludes that, “maybe ‘matured’ is a better word [i.e. than ‘evolution’] – because of the creativity of humans, not because of fortunate digital coincidences.” This explanation from the current leadership of the BRI helps to make sense of the variety of ways that people around the world are now speaking about the ‘growth,’ ’emergence,’ ‘maturing,’ ‘development,’ ‘advancement,’ ‘expansion’ and other ‘change-over-time’ metaphors to describe what is happening with distributed ledger technologies.

But What Are the Meanings of These Words?

Moving on to another writer and public figure, managing director of the IMF, Christine Lagarde similarly switches back and forth between ‘evolution’ and ‘revolution’ in seemingly an unsystematic way. She confirms that, “the fintech revolution questions the two forms of money we just discussed—coins and commercial bank deposits. And it questions the role of the state in providing money.” (2018)

She continues, however, saying, “I have tried to evaluate the case this morning for digital currency. The case is based on new and evolving requirements for money, as well as essential public policy objectives. My message is that while the case for digital currency is not universal, we should investigate it further, seriously, carefully, and creatively.” (Ibid)

One of the most prolific speakers and writers about blockchain, Andreas Antonopolous (2017), believes, “Over time, the way transaction fees are calculated and the effect they have on transaction prioritization has evolved. At first, transaction fees were fixed and constant across the network. Gradually, the fee structure relaxed and may be influenced by market forces, based on network capacity and transaction volume.” (2017: 127) … “Beyond bitcoin, the largest and most successful application of P2P technologies is file sharing, with Napster as the pioneer and BitTorrent as the most recent evolution of the architecture.” (Ibid: 171) He states that,

“the bitcoin network and software are constantly evolving, so consensus attacks would be met with immediate countermeasures by the bitcoin community, making bitcoin hardier, stealthier, and more robust than ever. … In order to evolve and develop the bitcoin system, the rules have to change from time to time to accommodate new features, improvements, or bug fixes. Unlike traditional software development, however, upgrades to a consensus system are much more difficult and require coordination between all the participants.” (Ibid: 256)

Further, he argues that, “Consensus software development continues to evolve and there is much discussion on the various mechanisms for changing the consensus rules.” (Ibid: 266) We thus see a major focus on ‘evolutionary’ blockchain change.

Yet in the final paragraph of the book, Antonopolous says, “We have examined just a few of the emerging applications that can be built using the bitcoin blockchain as a trust platform. These applications expand the scope of bitcoin beyond payments and beyond financial instruments, to encompass many other applications where trust is critical. By decentralizing the basis of trust, the bitcoin blockchain is a platform that will spawn many revolutionary applications in a wide variety of industries.” (Ibid: 304) The future of blockchain, therefore might be revolutionary based on many ‘evolutions’ of the technology.

In Life after Google: the Fall of Big Data and the Rise of the Blockchain Economy, George Gilder flip-flops back and forth between evolution and revolution with little apparent consistency, speaking about “the root-and-branch revolution of distributed peer-to-peer technology, which I call the ‘cryptocosm’,” (2018: 44) then stating that, “[t]he next wave of innovation will compress today’s parallel solutions in an evolutionary convergence of electronics and optics.” (Ibid: 58)

He suggests that, “[a] decentralized and open global rendering system is foundational for disruptive services and platforms to evolve from the post-mobile world of immersive computing, just as the open web was formed in the creation of Google, Amazon and Facebook.” (Ibid: 205) However, he also notes that, “Far beyond mere high-definition voice, 5G is the technological infrastructure for a coming revolution in networks. It enables new distributed security systems for the Internet of Things, the blockchain ledgers of the new crypto-economy of micropayments, and the augmented and virtual reality platforms of advanced Internet communications.” (Ibid: 231)

Gilder’s language seems to sometimes be more about appearance than substance, as he writes, “In the evolving technological economy, shaped by cryptographic innovations, Google is going to have to compete again.” (Ibid: 239) Further explaining, he notes that, “The revolution in cryptography has caused a great unbundling of the roles of money, promising to reverse the doldrums of the Google Age, which has been an epoch of bundling together, aggregating, all the digital assets of the world.” (Ibid: 256)

One key formulation renders his ideological views visible, reflecting his affiliation with the Discovery Institute: “The new system of the world must reverse these positions, exalting the singularities of creation: mind over matter, human consciousness over mechanism, real intelligence over mere algorithmic search, purposeful learning over mindless evolution, and truth over chance. A new system can open a heroic age of human accomplishment.” (Ibid: 272) Gilder seems to have no difficulty both denying and accepting ‘evolution’ at the same time, regardless of the fact that everyone agrees both ‘minds’ and ‘matter’ are involved in developing technologies.

Uncertainty Too From Financial Technology Leaders

Hanna Halaburda writes for the Bank of Canada (2018), saying, “The market’s excitement about blockchain technologies is growing and is perhaps best summarized in the increasingly popular slogan ‘blockchain revolution.’ It is estimated that the blockchain market size will grow from US$210 million in 2016 to over US$2 billion by 2021.” (2018: 1) Later in the paper she uses both terms, suggesting that,

“The broadening of the meaning of ‘blockchain’ to include smart contracts, encryption and distributed ledger could simply reflect the evolution of a term in a living language. However, precision matters for estimating costs and benefits, or even for predicting the best uses of blockchain technologies. Smart contracts, encryption and distributed ledger each bring different benefits. And since they can be implemented independently, an optimal solution for a particular application may include only some of these tools but not others. This may matter for the future of the blockchain revolution.” (Ibid: 5)

In conclusion, she accepts the same terminology as the Tapscotts, saying, “The blockchain revolution has brought distributed databases to the forefront and may result in wider adoption and new ideas for their use.” (Ibid: 9)

Andrea Pinna and Weibe Ruttenberg (2016) write that, “Over the last decade, information technology has contributed significantly to the evolution of financial markets, without, however, revolutionising the way in which financial institutions interact with one another. This may be about to change, as some market players are now predicting that new database technologies, such as blockchain and other distributed ledger technologies (DLTs), could be the source of an imminent revolution.” (Ibid: 2) “It is not yet, therefore, clear whether DLTs will cause a major revolution in mainstream financial markets or whether their use will remain limited to particular niches.” (Ibid: 32)

Former Chief Scientific Advisor to the British Government, Mark Walport (2016) suggests, “The development of block chain technology is but the first, though very important step towards a disruptive revolution in ledger technology that could transform the conduct of public and private sector organisations.” (2016: 10) He continues, “Regulation will need to evolve in parallel with the development of new implementations and applications of the technology” (Ibid: 12)

However, he also distinguishes a ‘revolutionary’ dimension to the technology, saying, “We are still at the early stages of an extraordinary post-industrial revolution driven by information technology. It is a revolution [that] is bringing important new benefits and risks. It is already clear that, within this revolution, the advent of distributed ledger technologies is starting to disrupt many of the existing ways of doing business.” (Ibid: 16)

And then he reverts back to evolutionary language, saying, “The terminology of this new field is still evolving, with many using the terms block chain (or blockchain), distributed ledger and shared ledger interchangeably.” (Ibid: 17) He emphasizes that, “M-Pesa challenged the notion that value transfer for exchange transactions had to be done through banks, and leapfrogged several developmental stages. But these innovations still rely on an existing hierarchical structure, using proprietary technology and trusted intermediaries. Though the change improves customer convenience, and significantly reduces costs to users and customers, this is evolution rather than revolution.” (Ibid: 54) Walport is one of the few voices insisting that changes in blockchain development are happening at a rather slower than rapid pace, which seems to determine his choice of terms.

Sam Town makes clear his preferred terminology between the two notions, stating, “While the ICO as it exists today may be gone tomorrow, the blockchain brings evolution, not revolution.” (2018) Here he seems to be suggesting that while ICOs may not last long as a credible method of fundraising, at least not without more stringent regulatory oversight, that nevertheless blockchain distributed ledger technologies will indeed have lasting and significant impact on finance and economics.

Does Evolution vs Revolution Matter?

Ugur Demirbas et al. (2018) also write to intentionally distinguish the two terms, saying, “In summary, while digital transformation shows disruptive influence on individual elements, its overall effect is rather evolutionary than revolutionary. The impact of DT in the context of the overarching corporate sourcing strategy is an incremental change than a disruptive creation of something completely new.” (2018: 8)

Again we see an explanation given that ‘evolutionary’ is preferred because of the pace (slow) and type (incremental) of change or the people’s aims and goals involved in developing the technology. They also indicate ‘disruption’ and ‘something completely new’ in their meaning of ‘revolutionary,’ which we will look at again below.

Jagjit Dhaliwal (2018) says that, “We all know that the Blockchain technology is revolutionizing our future by providing distributed networks, allowing peer-to-peer transactions without intermediaries. We have come a long way in a really short period of time from the inception of Bitcoin, one of the first cryptocurrencies based on Blockchain technology.”

He continues saying that, “Everyone is curious about which platform and cryptocurrency will win the race. The DLT landscape is changing rapidly and evolving really fast. I won’t be surprised if some of the solutions in this article will [sic] extinct soon.” Dhaliwal thus likewise shows that the pace of change impacts his choice of terms, though it is unclear how ‘rapid change’ and ‘fast evolution’ differ from ‘revolutionary.’

In a paper curiously named “The Evolution of Blockchain Development” (2017), the team at Alibaba Cloud similarly suggests that, “Blockchain as a technology has evolved rapidly in the past decade.” They continue, however, by appealing to readers: “Let us discuss a few major innovations that have revolutionized this field[11].” This is yet another example of the confusion in using the terms ‘evolution’ and ‘revolution’ when there is no clear explanation of what differentiates one from the other.

Megan Ray Nichols weighs in on the ‘revolution’ side, when she says, “blockchain is serving as a critical component in a major revolution that also includes rapid prototyping, lean manufacturing, 3D printing, & now blockchain-facilitated manufacturing & supply contracts.” (2018).

This and several of the examples above certainly do not refer to a ‘political revolution’ or ‘scientific revolution,’ but rather to an incoming ‘technological revolution’ that is supposedly happening all around us with ’emergent’ or ‘nascent’ new technologies, including, but not exclusive to blockchain. The hype surrounding blockchain with expectations in the near future, however, often seems to far exceed evidence of what has changed so far because of it.

Don Tapcott responded in an interview with McKinsey that, “the blockchain, the underlying technology, is the biggest innovation in computer science—the idea of a distributed database where trust is established through mass collaboration and clever code rather than through a powerful institution that does the authentication and the settlement[12].”

We have, of course, heard this kind of suggestive language before, so it’s not like predictions about ‘revolutionary technology’ are entirely new. One example of this harkens back to what Fred Brooks asked in 1975, if “technical developments that are most often advanced as potential silver bullets … offer revolutionary advances, or incremental ones?” (1975: 188) While not a few people have expressed inflated expectations for distributed ledger systems, we are still nevertheless waiting for a clear example of widespread usage of blockchain to be able to assess the variable speeds at which adoption can and likely will eventually take place.

With that basic background, we will now look at largely colloquial uses of the term ‘evolution’ as it relates to blockchain technology development.

Colloquial Usage of ‘Evolution’ for Blockchain Technology Development

A remarkable pattern among technology writers is to apply the term ‘evolution’ in what appears to be a basic colloquial way, suggesting no theoretical underpinning or technical meaning, and with no ideological implications. Instead, for these cases, the notion of ‘evolution’ is basically just used as a synonym for either ‘change’ (i.e. over time), ‘development,’ ‘creation’ or some kind of a general ‘process of history.’

Brigid McDermott, vice president of IBM blockchain business development, states:

“We’re asking companies to join to help evolve the solution and guide and steer its direction.”

“We’ll do PoCs [proofs-of-concept] later down the line.[1]” In this case, the verb ‘to evolve’ is meant in the same way as ‘to create,’ ‘to build’ or ‘to develop,’ without the notion of a natural genetic population, implication of a ‘struggle for life’ or ‘survival of the fittest,’ rates of mutation, variation, or other notions usually connected with ‘biological evolutionary theory.”

The Commonwealth of Learning suggests that, “When it comes to educational innovation, blockchains and ledgers are likely to lead to evolutionary gains[2].” While it is not entirely clear what they mean in this short report, we are likely supposed to gather a sense of ‘progress’ or ‘advancement’ in what they imply and suggest blockchain will lead to in the field of education.

Margaret Leigh Sinrod writes about blockchain for the World Economic Forum (2018). “The fact that banks are investing in this [blockchain] technology may sound fairly paradoxical,” she says, “given the context in which it evolved and gained traction.” In this case, the term ‘evolved’ seems to simply signify ‘history,’ i.e. that ‘something has happened’ and that blockchain now continues to persist as a phenomenon.

Dennis Sahlstrom similarly tells us that, “the evolution of blockchain arrived with Ethereum, created by Vitalik Buterin, which was an improvement of Bitcoin. This evolution added a further element which is the ability to build decentralized applications (dApps) and smart contracts to ensure that deals, transactions, and many other tasks can be performed without intermediaries.” (2018)

Here we see ‘evolution’ used as a way to symbolise a historical fact, again that ‘something has happened,’ thus indicating a new ‘stage’ of blockchain that also was ‘created. This approach might be confusing to people who accept a more technical meaning of ‘evolution’ as distinct from ‘creation’ or ‘intentional planning,’ almost sounding as if blockchain has taken on a life of its own.

John Dean Markunas from Power of Chain Consultancy continues this anthropomorphic language, suggesting that, “The [blockchain] technology itself will continue to evolve along with a wide variety of creative applications developed on top of it, similar to the development of the internet and world-wide-web[3].” This usage, while it signifies persistence and continuity, appears particularly confusing since the term ‘development’ is also used referring to the Internet, which other people claim has led to a ‘revolution’ in human society, as seen above.

Tadas Deksnys CEO and Founder of Unboxed writes that, “Though the future of ICOs is vague, the blockchain industry is still evolving and presenting new opportunities[4].” Again, we see here the notion of both history and continuity and that there is some kind of on-going process of unspecified speed, type or significance.

These are all common examples of people involved in or writing about the blockchain industry who suggest that blockchain demonstrates an ‘evolutionary’ rather than a ‘revolutionary,’ ‘developmental’ or otherwise ‘non-evolutionary’ process of change-over-time.

Frederik De Breuck (2019) says that, “its capabilities and platforms (both public and private) are rapidly evolving and blockchain and distributed ledgers remain for me (and many others) two of the most promising technology evolutions of recent decades for their potential to transform both society and enterprises.”

He uses other change-based concepts as well, such as emergence and extension, in the latter case saying, “[w]e think next year will see the ongoing evolution of these complex trust architectures and their extension beyond their organizational boundaries, into both ecosystems and society.” (Ibid) This language basically indicates something supposedly important is happening with blockchain, a description that it is growing and reaching more people in a community, network and/or ecosystem.

Reflections of What May Be Historical Precedents

Jesus Leal Trujilo et al. in their Deloitte paper (2017) base their logic in the ‘evolution’ of digital ecosystems, writing, “Our study appears to be the first empirical attempt to understand the evolution of blockchain using metadata available on GitHub … Our findings could help firms improve their ability to identify successful projects and opportunities based on how the blockchain ecosystem is evolving.” (2017: 2)

They also address the time period in terms of stages of development, saying, “At the current evolutionary stage of blockchain technology, it is likely to be in a developer’s best interest to develop, or watch the development of, blockchain solutions on open source. Blockchain appears to have a better chance to more quickly achieve rigorous protocols and standardization through open-source collaboration, which could make developing permissioned blockchains easier and better.” (Ibid: 5)

They continue, “The data scientists of Deloitte developed and honed a methodology to analyze and organize GitHub data in order to better understand the evolution of a young, possibly transformative technology and its ecosystem.” (Ibid: 15) They conclude saying, “It is our hope that these findings can arm the financial services industry with the data it may need to not only better identify successful projects and opportunities based on how the blockchain ecosystem is evolving, but to become influential participants, themselves, in how blockchain evolves.” (Ibid: 15) Thus, the promote both the development and so-called ‘evolution’ of blockchain technology based on the language of ‘ecosystem’ that loosely mimics biology.

The Systems Academy suggests about blockchain technology that, “over the past years it has been evolving fast, from the original Bitcoin protocol to the second generation Ethereum platform, to today where we are in the process of building what some call blockchain 3.0. In this evolution we can see how the technology is evolving from its initial form as essentially just a database, to becoming a fully-fledged globally distributed cloud computer.”

They add to others in this paper who suggest that, “The development and adoption of the Ethereum platform was a major step forward in the evolution of blockchain technology[5],” suggesting a kind of ‘progress’ narrative that switches between ‘development’ and ‘evolution’ and indicates improvement rather than replacement or destruction of the old system.

Stapels et al. flip back and forth between ‘development’ and ‘evolution,’ stating that, “blockchains are still a rapidly evolving technology, with ongoing developments, especially to improve scalability and confidentiality. Globally, governments, enterprises, and startups are exploring the technology/market fit in a wide variety of use cases and for a wide variety of requirements and regulatory demands.” They also suggest a present lack of knowledge towards building and maintaining trust among blockchain users, saying “There is still much that is unknown about the development of trustworthy blockchain-based systems.” (2018: 1)

Bryan Zhang writes in the Foreword to Rauchs et al. 2018, that, “the landscape of DLT itself continues its swift evolution.” Again, we see the suggestion of a continuity of some kind, as if we are in a historical period of flux and change with the rise of DLTs. In conclusion, the authors state that, “Nearly 10 years after Bitcoin entered the world, the DLT ecosystem is still in early stages: it is constantly evolving and characterised by relentless experimentation and R&D.” (2018: 92)

This usage doesn’t necessarily imply that Bitcoin arrived on its own without a creative inventor or network of users, but rather that it’s simply in a process that has yet to reach its conclusion and thus should be thought of as impermanent or temporary.

ElBarhrawy et al. (2017) “Here, we present a first complete analysis of the cryptocurrency market, considering its evolution between April 2013 and May 2017.” (Ibid: 2) They then suggest there is a theoretical underpinning one can use to study this historical period involving cryptocurrencies. “By adopting an ecological perspective, we have pointed out that the neutral model of evolution captures several of the observed properties of the market.” (Ibid: 7)

In this approach we again see usage of the term ‘evolution’ to mean ‘history,’ yet in a broader way that combines economics with ecology and push the idea of ‘ecosystem’ thinking that is also front and centre in much of the ideological blockchain evolutionism below.

Contact details: gregory.sandstrom@gmailcom

References

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Wright, Craig Steven (2019a). https://medium.com/@craig_10243/proof-of-work-1a323e82fd9

Videos

“Alex Tapscott: Blockchain Revolution | Talks at Google” – https://www.youtube.com/watch?v=3PdO7zVqOwc

“Are Blockchains Alive? Co-evolving with Technology” – Amanda Gutterman (ConsenSys) – https://www.youtube.com/watch?v=X7GkkGTnVwA

“Block Chain Revolution | Giovanna Fessenden | TEDxBerkshires” – https://www.youtube.com/watch?v=oMhZTEQZJPI

“Bitcoin and the history of money” – “Let’s take a look at the evolution of money.” – https://www.youtube.com/watch?v=IP0jCjyrew8

“Blockchain – evolution or revolution?” –  https://www.youtube.com/watch?v=LojzPukAtmM

“Blockchain Evolution & Empowerment” – https://www.youtube.com/watch?v=eSUC9NFccNk

“Blockchain Evolution 2” – Reese Jones – https://www.youtube.com/watch?v=mCPqXHt-z0k

“Blockchain Evolution or Revolution in the Luxembourg Financial Place? – Nicolas Carey https://www.youtube.com/watch?v=Wp9FB_JQlgI

“Blockchain Evolution” – https://www.youtube.com/watch?v=CULUqgfVteg

“Blockchain Evolution” – Complexity Labs – https://www.youtube.com/watch?v=rO2LSBDekvE

“Blockchains’ Evolution by natural selection like biology’s genetics” – Reese Jones – https://www.youtube.com/watch?v=4JEFGtsu0s4

“Blockchain Evolution” – https://www.youtube.com/watch?v=tGcuJoFZLOY

“Chandler Guo on The Bitcoin & Blockchain Revolution” – https://www.youtube.com/watch?v=J7g2JFn68LU

“Cryptos Are The EVOLUTION of Money and Blockchain is the REVOLUTION of Trust! Vlog#18” – Siam Kidd – https://www.youtube.com/watch?v=-nu2F6_K0S0

“DigiByte Blockchain – The evolution of the Internet & the revolution in the financial systems” – https://www.youtube.com/watch?v=w8h10ckU0sE “The revolution has already begun.”

“Don Tapscott – The Blockchain Revolution – https://www.youtube.com/watch?v=gZEmaSbqfYQ

“Evolution of Bitcoin” – Documentary Film – https://www.youtube.com/watch?v=HUpGHOLkoXs

“Evolution of Blockchain And Its Future Moving Forward In 2018!” – https://www.youtube.com/watch?v=YWlMoxMTbDQ

“Evolution of Blockchain in India:The value of Ownership.” – Mr.Akash Gaurav – TEDxKIITUniversity – https://www.youtube.com/watch?v=BtTJmb0jYzE

“Evolution of the Blockchain Economy” – Jeremy Gardner – Startup Grind – https://www.youtube.com/watch?v=Q7cPy6ITUm4

“Future Evolution of Blockchain” – Silicon Valley TV – https://www.youtube.com/watch?v=5_6m7LYIEo4

“Future Thinkers Podcast – a podcast about evolving technology, society and consciousness. https://futurethinkers.org/

“Genetics of Blockchain Evolution” – Reese Jones – https://www.youtube.com/watch?v=8fFsmuvyXeE

“Keynote: Blockchain’s Evolution: Digital Assets are getting Physical” – FinTech Worldwide” – https://www.youtube.com/watch?v=1p5PUn4z_Gs

“How the Blockchain revolution will change our lives? | Eddy Travia | TEDxIEMadrid” https://www.youtube.com/watch?v=ErxKm0b0DIU

“How the Blockchain Revolution Will Decentralize Power and End Corruption | Brian Behlendorf” https://www.youtube.com/watch?v=Tv-XR6gXfLI

“Interview for Bitcoin And Blockchain Evolution Podcast – Sarah Herring – “Evolution – There is a Revolution coming!” https://www.youtube.com/watch?v=tIZJsFotDdg

“John McAfee on Infowars: Nothing Can Stop The Blockchain Revolution” – https://www.youtube.com/watch?v=CssU9WBHx6k

“Make the blockchain business case: Evolution, not revolution” (only title, not in video) – PWC – https://www.youtube.com/watch?v=sjr_Wqwk1SI

“The blockchain evolution, from services…to smartphones.” – Mingis on Tech – https://www.youtube.com/watch?v=jvn5zZj5IR8

“The Blockchain Evolution” – Hewlett Packard – https://www.hpe.com/us/en/insights/videos/the-evolution-of-blockchain-1712.html

“The Blockchain Evolution” – https://www.youtube.com/watch?v=TeyeKXmqQn8

“The Blockchain Evolution” – Cambridge House International” – https://www.youtube.com/watch?v=nELBTdqeKuQ

“The Evolution of Bitcoin – Bill Barhydt – Global Summit 2018 | Singularity” Universityhttps://www.youtube.com/watch?v=CZjK1i9CE6U

“The Evolution of Blockchain and Global Vision (Shanghai)” https://www.youtube.com/watch?v=56rOLarCttA

“The Evolution Of Blockchain Over The Decades” – With David Birch” https://www.youtube.com/watch?v=yC8oBJSQ6vc

“The Evolution of Blockchain technology” – Amir Assif. Microsoft Israel” – https://www.youtube.com/watch?v=f_eKp1z5hj0

“The Evolution of Blockchain: How EOS is reinventing blockchain” – https://www.youtube.com/watch?v=R8aDGf8WpKs

“The Evolution of Blockchain” – Nicola Morris – https://www.youtube.com/watch?v=aSy-UJn1G1I

“The Evolution of Blockchain” – The State of Digital Money 18′ conference” – https://www.youtube.com/watch?v=RWfNVTgbqjc

“The Blockchain Revolution – Graham Richter, Accenture” – https://www.youtube.com/watch?v=AYTmjZmsUm4

“The Blockchain Revolution | Rajesh Dhuddu | TEDxHyderabad” – https://www.youtube.com/watch?v=OrnvX92vlu8

“The Blockchain Revolution by Talal Tabaa – ECOH 2018” – https://www.youtube.com/watch?v=AvRJ1kEQ2so

“The Blockchain Revolution Changing the Rules https://www.youtube.com/watch?v=GTgG8XzcVC0

“The Blockchain Revolution in Business and Finance” – https://www.youtube.com/watch?v=3SUfz6p0a7Y

“The blockchain revolution, the ultimate industry disruptor” – https://www.youtube.com/watch?v=7hEiHR-K_KY

“The Blockchain Revolution: From Organisations to Organism | Matan Field | TEDxBreda” – https://www.youtube.com/watch?v=2OSbseTJWfY

[1] Nov. 14, 2008. https://satoshi.nakamotoinstitute.org/emails/cryptography/12/

[2] Your author of this paper received his degree in ‘Sociological Sciences’ from St. Petersburg State University in Russia, after a dissertation defense at the Sociological Institute of the Russian Academy of Science in 2010.

[3] “The gap between biological evolution and artificial systems evolution is just too enormous to expect to link the two.” – Meir Lehman (In Williams, 2002)

[4] It is most likely that none of the authors cited in this study was thinking about ‘young earth creationism’ as a position that they aimed to oppose by using the term ‘evolution.’ Similarly, no theory of ‘Intelligent Design’ as an alternative to ‘neo-Darwinism’ is at the heart of this paper’s rejection of ‘technological evolutionary’ theories.

[5] “When I use a word,” Humpty Dumpty said, in rather a scornful tone, “it means just what I choose it to mean—neither more nor less.” – Lewis Carroll (Through the Looking-Glass, 1872)

[6] Private conversation 07-02-2019.

[7] “In software engineering there is no theory. It’s all arm flapping and intuition. I believe that a theory of software evolution could eventually translate into a theory of software engineering. Either that or it will come very close. It will lay the foundation for a wider theory of software evolution.” – Lehman (In Williams 2002)

[8] “This is one of the hardest lessons for humans to learn. We cannot admit that things might be neither good nor evil, neither cruel nor kind, but simply callous – indifferent to all suffering, lacking all purpose.” … “The universe we observe has precisely the properties we should expect if there is, at bottom, no design, no purpose, no evil and no good, nothing but blind pitiless indifference.” – Richard Dawkins (River Out of Eden. Basic Books, New York, 1995: 95)

[9] https://money.cnn.com/2018/02/21/technology/canada-india-blockchain-partnership-bri-nasscom/index.html

[10] Private email, 24-02-2019.

[11] https://www.alibabacloud.com/blog/The-Evolution-of-Blockchain-Development_p73812

[12] http://www.mckinsey.com/industries/high-tech/our-insights/how-blockchains-could-change-the-world

[1] http://fortune.com/2017/08/22/walmart-blockchain-ibm-food-nestle-unilever-tyson-dole/

[2] https://www.col.org/news/news/col-promotes-blockchain-education

[3] https://www.linkedin.com/pulse/emancipation-from-ball-chain-blockchain-john-dean-markunas

[4] https://medium.com/unboxed-network/our-journey-so-far-unboxed-airdrop-update-72b63ab52631

[5] http://complexitylabs.io/evolution-of-blockchain/

Author Information: Brian Martin, University of Wollongong, bmartin@uow.edu.au.

Martin, Brian. “Bad Social Science.” Social Epistemology Review and Reply Collective 8, no. 3 (2019): 6-16.

The pdf of the article gives specific page references. Shortlink: https://wp.me/p1Bfg0-47a

Image by Sanofi Pasteur via Flickr / Creative Commons

 

People untrained in social science frameworks and methods often make assumptions, observations or conclusions about the social world.[1] For example, they might say, “President Trump is a psychopath,” thereby making a judgement about Trump’s mental state. The point here is not whether this judgement is right or wrong, but whether it is based on a careful study of Trump’s thoughts and behaviour drawing on relevant expertise.

In most cases, the claim “President Trump is a psychopath” is bad psychology, in the sense that it is a conclusion reached without the application of skills in psychological diagnosis expected among professional psychologists and psychiatrists.[2] Even a non-psychologist can recognise cruder forms of bad psychology: they lack the application of standard tools in the field, such as comparison of criteria for psychopathy with Trump’s thought and behaviour.

“Bad social science” here refers to claims about society and social relationships that fall very far short of what social scientists consider good scholarship. This might be due to using false or misleading evidence, making faulty arguments, drawing unsupported conclusions or various other severe methodological, empirical or theoretical deficiencies.

In all sorts of public commentary and private conversations, examples of bad social science are legion. Instances are so common that it may seem pointless to take note of problems with ill-informed claims. However, there is value in a more systematic examination of different sorts of everyday bad social science. Such an examination can point to what is important in doing good social science and to weaknesses in assumptions, evidence and argumentation. It can also provide insights into how to defend and promote high-quality social analysis.

Here, I illustrate several facets of bad social science found in a specific public scientific controversy: the Australian vaccination debate. It is a public debate in which many partisans make claims about social dynamics, so there is ample material for analysis. In addition, because the debate is highly polarised, involves strong emotions and is extremely rancorous, it is to be expected that many deviations from calm, rational, polite discourse would be on display.

Another reason for selecting this topic is that I have been studying the debate for quite a number of years, and indeed have been drawn into the debate as a “captive of controversy.”[3] Several of the types of bad social science are found on both sides of the debate. Here, I focus mainly on pro-vaccination campaigners for reasons that will become clear.

In the following sections, I address several facets of bad social science: ad hominem attacks, not defining terms, use of limited and dubious evidence, misrepresentation, lack of reference to alternative viewpoints, lack of quality control, and drawing of unjustified conclusions. In each case, I provide examples from the Australian public vaccination debate, drawing on my experience. In a sense, selecting these topics represents an informal application of grounded theory: each of the shortcomings became evident to me through encountering numerous instances. After this, I note that there is a greater risk of deficient argumentation when defending orthodoxy.

With this background, I outline how studying bad social science can be of benefit in three ways: as a pointer to particular areas in which it is important to maintain high standards, as a toolkit for responding to attacks on social science, and as a reminder of the need to improve public understanding of social science approaches.

Ad Hominem

In the Australian vaccination debate, many partisans make adverse comments about opponents as a means of discrediting them. Social scientists recognise that ad hominem argumentation, namely attacking the person rather than dealing with what they say, is illegitimate for the purposes of making a case.

In the mid 1990s, Meryl Dorey founded the Australian Vaccination Network (AVN), which became the leading citizens’ group critical of government vaccination policy.[4] In 2009, a pro-vaccination citizens’ group called Stop the Australian Vaccination Network (SAVN) was set up with the stated aim of discrediting and shutting down the AVN.[5] SAVNers referred to Dorey with a wide range of epithets, for example “cunt.”[6]

What is interesting here is that some ad hominem attacks contain an implicit social analysis. One of them is “liar.” SAVNer Ken McLeod accused Dorey of being a liar, giving various examples.[7] However, some of these examples show only that Dorey persisted in making claims that SAVNers believed had been refuted.[8] This does not necessarily constitute lying, if lying is defined, as it often is by researchers in the area, as consciously intending to deceive.[9] To the extent that McLeod failed to relate his claims to research in the field, his application of the label “liar” constitutes bad social science.

Another term applied to vaccine critics is “babykiller.” In the Australian context, this word contains an implied social analysis, based on these premises: public questioning of vaccination policy causes some parents not to have their children vaccinated, leading to reduced vaccination rates and thence to more children dying of infectious diseases.

“Babykiller” also contains a moral judgement, namely that public critics of vaccination are culpable for the deaths of children from vaccination-preventable diseases. Few of those applying the term “babykiller” provide evidence to back up the implicit social analysis and judgement, so the label in these instances represents bad social science.

There are numerous other examples of ad hominem in the vaccination debate, on both sides. Some of them might be said to be primarily abuse, such as “cunt.” Others, though, contain an associated or implied social analysis, so to judge its quality it is necessary to assess whether the analysis conforms to conventions within social science.

Undefined terms

In social science, it is normal to define key concepts, either by explicit definitions or descriptive accounts. The point is to provide clarity when the concept is used.

One of the terms used by vaccination supporters in the Australian debate is “anti-vaxxer.” Despite the ubiquity of this term in social and mass media, I have never seen it defined. This is significant because of the considerable ambiguity involved. “Anti-vaxxer” might refer to parents who refuse all vaccines for their children and themselves, parents who have their children receive some but not all recommended vaccines, parents who express reservations about vaccination, and/or campaigners who criticise vaccination policy.

The way “anti-vaxxer” is applied in practice tends to conflate these different meanings, with the implication that any criticism of vaccination puts you in the camp of those who refuse all vaccines. The label “anti-vaxxer” has been applied to me even though I do not have a strong view about vaccination.[10]

Because of the lack of a definition or clear meaning, the term “anti-vaxxer” is a form of ad hominem and also represents bad social science. Tellingly, few social scientists studying the vaccination issue use the term descriptively.

In their publications, social scientists may not define all the terms they use because their meanings are commonly accepted in the field. Nearly always, though, some researchers pay close attention to any widely used concept.[11] When such a concept remains ill-defined, this may be a sign of bad social science — especially when it is used as a pejorative label.

Limited and Dubious Evidence

Social scientists normally seek to provide strong evidence for their claims and restrict their claims to what the evidence can support. In public debates, this caution is often disregarded.

After SAVN was formed in 2009, one of its initial claims was that the AVN believed in a global conspiracy to implant mind-control chips via vaccinations. The key piece of evidence SAVNers provided to support this claim was that Meryl Dorey had given a link to the website of David Icke, who was known to have some weird beliefs, such as that the world is ruled by shape-shifting reptilian humanoids.

The weakness of this evidence should be apparent. Just because Icke has some weird beliefs does not mean every document on his website involves adherence to weird beliefs, and just because Dorey provided a link to a document does not prove she believes in everything in the document, much less subscribes to the beliefs of the owner of the website. Furthermore, Dorey denied believing in a mind-control global conspiracy.

Finally, even if Dorey had believed in this conspiracy, this does not mean other members of the AVN, or the AVN as an organisation, believed in the conspiracy. Although the evidence was exceedingly weak, several SAVNers, after I confronted them on the matter, initially refused to back down from their claims.[12]

Misrepresentation

When studying an issue, scholars assume that evidence, sources and other material should be represented fairly. For example, a quotation from an author should fairly present the author’s views, and not be used out of context to show something different than what the author intended.

Quite a few campaigners in the Australian vaccination debate use a different approach, which might be called “gotcha”. Quotes are used to expose writers as incompetent, misguided or deluded. Views of authors are misrepresented as a means of discrediting and dismissing them.

Judy Wilyman did her PhD under my supervision and was the subject of attack for years before she graduated. On 13 January 2016, just two days after her thesis was posted online, it was the subject of a front-page story in the daily newspaper The Australian. The journalist, despite having been informed of a convenient summary of the thesis, did not mention any of its key ideas, instead claiming that it involved a conspiracy theory. Quotes from the thesis, taken out of context, were paraded as evidence of inadequacy.

This journalistic misrepresentation of Judy’s thesis was remarkably influential. It led to a cascade of hostile commentary, with hundreds of online comments on the numerous stories in The Australian, an online petition signed by thousands of people, and calls by scientists for Judy’s PhD to be revoked. In all the furore, not a single critic of her thesis posted a fair-minded summary of its contents.[13]

Alternative Viewpoints?

In high-quality social science, it is common to defend a viewpoint, but considered appropriate to examine other perspectives. Indeed, when presenting a critique, it is usual to begin with a summary of the work to be criticised.

In the Australian vaccination debate, partisans do not even attempt to present the opposing side’s viewpoint. I have never seen any campaigner provide a summary of the evidence and arguments supporting the opposition’s viewpoint. Vaccination critics present evidence and arguments that cast doubt on the government’s vaccination policy, and never try to summarise the evidence and arguments supporting it. Likewise, backers of the government’s policy never try to summarise the case against it.

There are also some intermediate viewpoints, divergent from the entrenched positions in the public debate. For example, there are some commentators who support some vaccines but not all the government-recommended ones, or who support single vaccines rather than multiple vaccines. These non-standard positions are hardly ever discussed in public by pro-vaccination campaigners.[14] More commonly, they are implicitly subsumed by the label “anti-vaxxer.”

To find summaries of arguments and evidence on both sides, it is necessary to turn to work by social scientists, and then only the few of them studying the debate without arguing for one side or the other.[15]

Quality Control

When making a claim, it makes sense to check it. Social scientists commonly do this by checking sources and/or by relying on peer review. For contemporary issues, it’s often possible to check with the person who made the claim.

In the Australian vaccination debate, there seems to be little attempt to check claims, especially when they are derogatory claims about opponents. I can speak from personal experience. Quite a number of SAVNers have made comments about my work, for example in blogs. On not a single occasion has any one of them checked with me in advance of publication.

After SAVN was formed and I started writing about free speech in the Australian vaccination debate, I sent drafts of some of my papers to SAVNers for comment. Rather than using this opportunity to send me corrections and comments, the response was to attack me, including by making complaints to my university.[16] Interestingly, the only SAVNer to have been helpful in commenting on drafts is another academic.

Another example concerns Andrew Wakefield, a gastroenterologist who was lead author of a paper in The Lancet suggesting that the possibility that the MMR triple vaccine (measles, mumps and rubella) might be linked to autism should be investigated. The paper led to a storm of media attention.

Australian pro-vaccination campaigns, and quite a few media reports, refer to Wakefield’s alleged wrongdoings, treating them as discrediting any criticism of vaccination. Incorrect statements about Wakefield are commonplace, for example that he lost his medical licence due to scientific fraud. It is a simple matter to check the facts, but apparently few do this. Even fewer take the trouble to look into the claims and counterclaims about Wakefield and qualify their statements accordingly.[17]

Drawing Conclusions

Social scientists are trained to be cautious in drawing conclusions, ensuring that they do not go beyond what can be justified from data and arguments. In addition, it is standard to include a discussion of limitations. This sort of caution is often absent in public debates.

SAVNers have claimed great success in their campaign against the AVN, giving evidence that, for example, their efforts have prevented AVN talks from being held and reduced media coverage of vaccine critics. However, although AVN operations have undoubtedly been hampered, this does not necessarily show that vaccination rates have increased or, more importantly, that public health has benefited.[18]

Defending Orthodoxy

Many social scientists undertake research in controversial areas. Some support the dominant views, some support an unorthodox position and quite a few try not to take a stand. There is no inherent problem in supporting the orthodox position, but doing so brings greater risks to the quality of research.

Many SAVNers assume that vaccination is a scientific issue and that only people with scientific credentials, for example degrees or publications in virology or epidemiology, have any credibility. This was apparent in an article by philosopher Patrick Stokes entitled “No, you’re not entitled to your opinion” that received high praise from SAVNers.[19] It was also apparent in the attack on Judy Wilyman, whose PhD was criticised because it was not in a scientific field, and because she analysed scientific claims without being a scientist. The claim that only scientists can validly criticise vaccination is easily countered.[20] The problem for SAVNers is that they are less likely to question assumptions precisely because they support the dominant viewpoint.

There is a fascinating aspect to campaigners supporting orthodoxy: they themselves frequently make claims about vaccination although they are not scientists with relevant qualifications. They do not apply their own strictures about necessary expertise to themselves. This can be explained as deriving from “honour by association,” a process parallel to guilt by association but less noticed because it is so common. In honour by association, a person gains or assumes greater credibility by being associated with a prestigious person, group or view.

Someone without special expertise who asserts a claim that supports orthodoxy implicitly takes on the mantle of the experts on the side of orthodoxy. It is only those who challenge orthodoxy who are expected to have relevant credentials. There is nothing inherently wrong with supporting the orthodox view, but it does mean there is less pressure to examine assumptions.

My initial example of bad social science was calling Donald Trump a psychopath. Suppose you said Trump has narcissistic personality disorder. This might not seem to be bad social science because it accords with the views of many psychologists. However, agreeing with orthodoxy, without accompanying deployment of expertise, does not constitute good social science any more than disagreeing with orthodoxy.

Lessons

It is all too easy to identify examples of bad social science in popular commentary. They are commonplace in political campaigning and in everyday conversations.

Being attuned to common violations of good practice has three potential benefits: as a useful reminder to maintain high standards; as a toolkit for responding to attacks on social science; and as a guide to encouraging greater public awareness of social scientific thinking and methods.

Bad Social Science as a Reminder to Maintain High Standards

Most of the kinds of bad social science prevalent in the Australian vaccination debate seldom receive extended attention in the social science literature. For example, the widely used and cited textbook Social Research Methods does not even mention ad hominem, presumably because avoiding it is so basic that it need not be discussed.

It describes five common errors in everyday thinking that social scientists should avoid: overgeneralisation, selective observation, premature closure, the halo effect and false consensus.[21] Some of these overlap with the shortcomings I’ve observed in the Australian vaccination debate. For example, the halo effect, in which prestigious sources are given more credibility, has affinities with honour by association.

The textbook The Craft of Research likewise does not mention ad hominem. In a final brief section on the ethics of research, there are a couple of points that can be applied to the vaccination debate. For example, ethical researchers “do not caricature or distort opposing views.” Another recommendation is that “When you acknowledge your readers’ alternative views, including their strongest objections and reservations,” you move towards more reliable knowledge and honour readers’ dignity.[22] Compared with the careful exposition of research methods in this and other texts, the shortcomings in public debates are seemingly so basic and obvious as to not warrant extended discussion.

No doubt many social scientists could point to the work of others in the field — or even their own — as failing to meet the highest standards. Looking at examples of bad social science can provide a reminder of what to avoid. For example, being aware of ad hominem argumentation can help in avoiding subtle denigration of authors and instead focusing entirely on their evidence and arguments. Being reminded of confirmation bias can encourage exploration of a greater diversity of viewpoints.

Malcolm Wright and Scott Armstrong examined 50 articles that cited a method in survey-based research that Armstrong had developed years earlier. They discovered that only one of the 50 studies had reported the method correctly. They recommend that researchers send drafts of their work to authors of cited studies — especially those on which the research depends most heavily — to ensure accuracy.[23] This is not a common practice in any field of scholarship but is worth considering in the interests of improving quality.

Bad Social Science as a Toolkit for Responding to Attacks

Alan Sokal wrote an intentionally incoherent article that was published in 1996 in the cultural studies journal Social Text. Numerous commentators lauded Sokal for carrying out an audacious prank that revealed the truth about cultural studies, namely that it was bunk. These commentators had not carried out relevant studies themselves, nor were most of them familiar with the field of cultural studies, including its frameworks, objects of study, methods of analysis, conclusions and exemplary pieces of scholarship.

To the extent that these commentators were uninformed about cultural studies yet willing to praise Sokal for his hoax, they were involved in a sort of bad social science. Perhaps they supported Sokal’s hoax because it agreed with their preconceived ideas, though investigation would be needed to assess this hypothesis.

Most responses to the hoax took a defensive line, for example arguing that Sokal’s conclusions were not justified. Only a few argued that interpreting the hoax as showing the vacuity of cultural studies was itself poor social science.[24] Sokal himself said it was inappropriate to draw general conclusions about cultural studies from the hoax,[25] so ironically it would have been possible to respond to attackers by quoting Sokal.

When social scientists come under attack, it can be useful to examine the evidence and methods used or cited by the attackers, and to point out, as is often the case, that they fail to measure up to standards in the field.

Encouraging Greater Public Awareness of Social Science Thinking and Methods

It is easy to communicate with like-minded scholars and commiserate about the ignorance of those who misunderstand or wilfully misrepresent social science. More challenging is to pay close attention to the characteristic ways in which people make assumptions and reason about the social world and how these ways often fall far short of the standards expected in scholarly circles.

By identifying common forms of bad social science, it may be possible to better design interventions into public discourse to encourage more rigorous thinking about evidence and argument, especially to counter spurious and ill-founded claims by partisans in public debates.

Conclusion

Social scientists, in looking at research contributions, usually focus on what is high quality: the deepest insights, the tightest arguments, the most comprehensive data, the most sophisticated analysis and the most elegant writing. This makes sense: top quality contributions offer worthwhile models to learn from and emulate.

Nevertheless, there is also a role for learning from poor quality contributions. It is instructive to look at public debates involving social issues in which people make judgements about the same sorts of matters that are investigated by social scientists, everything from criminal justice to social mores. Contributions to public debates can starkly show flaws in reasoning and the use of evidence. These flaws provide a useful reminder of things to avoid.

Observation of the Australian vaccination debate reveals several types of bad social science, including ad hominem attacks, failing to define terms, relying on dubious sources, failing to provide context, and not checking claims. The risk of succumbing to these shortcomings seems to be magnified when the orthodox viewpoint is being supported, because it is assumed to be correct and there is less likelihood of being held accountable by opponents.

There is something additional that social scientists can learn by studying contributions to public debates that have serious empirical and theoretical shortcomings. There are likely to be characteristic failures that occur repeatedly. These offer supplementary guidance for what to avoid. They also provide insight into what sort of training, for aspiring social scientists, is useful for moving from unreflective arguments to careful research.

There is also a challenge that few scholars have tackled. Given the prevalence of bad social science in many public debates, is it possible to intervene in these debates in a way that fosters greater appreciation for what is involved in good quality scholarship, and encourages campaigners to aspire to make sounder contributions?

Contact details: bmartin@uow.edu.au

References

Blume, Stuart. Immunization: How Vaccines Became Controversial. London: Reaktion Books, 2017.

Booth, Wayne C.; Gregory G. Colomb, Joseph M. Williams, Joseph Bizup and William T. FitzGerald, The Craft of Research, fourth edition. Chicago: University of Chicago Press, 2016.

Collier, David; Fernando Daniel Hidalgo and Andra Olivia Maciuceanu, “Essentially contested concepts: debates and applications,” Journal of Political Ideologies, 11(3), October 2006, pp. 211–246.

Ekman, Paul. Telling Lies: Clues to Deceit in the Marketplace, Politics, and Marriage. New York: Norton, 1985.

Hilgartner, Stephen, “The Sokal affair in context,” Science, Technology, & Human Values, 22(4), Autumn 1997, pp. 506–522.

Lee, Bandy X. The Dangerous Case of Donald Trump: 27 Psychiatrists and Mental Health Experts Assess a President. New York: St. Martin’s Press, 2017.

Martin, Brian; and Florencia Peña Saint Martin. El mobbing en la esfera pública: el fenómeno y sus características [Public mobbing: a phenomenon and its features]. In Norma González González (Coordinadora), Organización social del trabajo en la posmodernidad: salud mental, ambientes laborales y vida cotidiana (Guadalajara, Jalisco, México: Prometeo Editores, 2014), pp. 91-114.

Martin, Brian. “Debating vaccination: understanding the attack on the Australian Vaccination Network.” Living Wisdom, no. 8, 2011, pp. 14–40.

Martin, Brian. “On the suppression of vaccination dissent.” Science & Engineering Ethics. Vol. 21, No. 1, 2015, pp. 143–157.

Martin, Brian. Evidence-based campaigning. Archives of Public Health, 76, no. 54. (2018), https://doi.org/10.1186/s13690-018-0302-4.

Martin, Brian. Vaccination Panic in Australia. Sparsnäs, Sweden: Irene Publishing, 2018.

Ken McLeod, “Meryl Dorey’s trouble with the truth, part 1: how Meryl Dorey lies, obfuscates, prevaricates, exaggerates, confabulates and confuses in promoting her anti-vaccination agenda,” 2010, http://www.scribd.com/doc/47704677/Meryl-Doreys-Trouble-With-the-Truth-Part-1.

Neuman, W. Lawrence. Social Research Methods: Qualitative and Quantitative Approaches, seventh edition. Boston, MA: Pearson, 2011.

Scott, Pam; Evelleen Richards and Brian Martin, “Captives of controversy: the myth of the neutral social researcher in contemporary scientific controversies,” Science, Technology, & Human Values, Vol. 15, No. 4, Fall 1990, pp. 474–494.

Sokal, Alan D. “What the Social Text affair does and does not prove,” in Noretta Koertge (ed.), A House Built on Sand: Exposing Postmodernist Myths about Science (New York: Oxford University Press, 1998), pp. 9–22

Stokes, Patrick. “No, you’re not entitled to your opinion,” The Conversation, 5 October 2012, https://theconversation.com/no-youre-not-entitled-to-your-opinion-9978.

Wright, Malcolm, and J. Scott Armstrong, “The ombudsman: verification of citations: fawlty towers of knowledge?” Interfaces, 38 (2), March-April 2008.

[1] Thanks to Meryl Dorey, Stephen Hilgartner, Larry Neuman, Alan Sokal and Malcolm Wright for valuable feedback on drafts.

[2] For informed commentary on these issues, see Bandy X. Lee, The Dangerous Case of Donald Trump: 27 Psychiatrists and Mental Health Experts Assess a President (New York: St. Martin’s Press, 2017).

[3] Pam Scott, Evelleen Richards and Brian Martin, “Captives of controversy: the myth of the neutral social researcher in contemporary scientific controversies,” Science, Technology, & Human Values, Vol. 15, No. 4, Fall 1990, pp. 474–494.

[4] The AVN, forced to change its name in 2014, became the Australian Vaccination-skeptics Network. In 2018 it voluntarily changed its name to the Australian Vaccination-risks Network.

[5] In 2014, SAVN changed its name to Stop the Australian (Anti-)Vaccination Network.

[6] Brian Martin and Florencia Peña Saint Martin. El mobbing en la esfera pública: el fenómeno y sus características [Public mobbing: a phenomenon and its features]. In Norma González González (Coordinadora), Organización social del trabajo en la posmodernidad: salud mental, ambientes laborales y vida cotidiana (Guadalajara, Jalisco, México: Prometeo Editores, 2014), pp. 91-114.

[7] Ken McLeod, “Meryl Dorey’s trouble with the truth, part 1: how Meryl Dorey lies, obfuscates, prevaricates, exaggerates, confabulates and confuses in promoting her anti-vaccination agenda,” 2010, http://www.scribd.com/doc/47704677/Meryl-Doreys-Trouble-With-the-Truth-Part-1.

[8] Brian Martin, “Debating vaccination: understanding the attack on the Australian Vaccination Network,” Living Wisdom, no. 8, 2011, pp. 14–40, at pp. 28–30.

[9] E.g., Paul Ekman, Telling Lies: Clues to Deceit in the Marketplace, Politics, and Marriage (New York: Norton, 1985).

[10] On Wikipedia I am categorised as an “anti-vaccination activist,” a term that is not defined on the entry listing those in the category. See Brian Martin, “Persistent bias on Wikipedia: methods and responses,” Social Science Computer Review, Vol. 36, No. 3, June 2018, pp. 379–388.

[11] See for example David Collier, Fernando Daniel Hidalgo and Andra Olivia Maciuceanu, “Essentially contested concepts: debates and applications,” Journal of Political Ideologies, 11(3), October 2006, pp. 211–246.

[12] Brian Martin. “Caught in the vaccination wars (part 3)”, 23 October 2012, http://www.bmartin.cc/pubs/12hpi-comments.html.

[13] The only possible exception to this statement is Michael Brull, “Anti-vaccination cranks versus academic freedom,” New Matilda, 7 February 2016, who reproduced my own summary of the key points in the thesis relevant to Australian government vaccination policy. For my responses to the attack, see http://www.bmartin.cc/pubs/controversy.html – Wilyman, for example “Defending university integrity,” International Journal for Educational Integrity, Vol. 13, No. 1, 2017, pp. 1–14.

[14] Brian Martin, Vaccination Panic in Australia (Sparsnäs, Sweden: Irene Publishing, 2018), pp. 15–24.

[15] E.g., Stuart Blume, Immunization: How Vaccines Became Controversial (London: Reaktion Books, 2017).

[16] Brian Martin. “Caught in the vaccination wars”, 28 April 2011, http://www.bmartin.cc/pubs/11savn/.

[17] For own commentary on Wakefield, see “On the suppression of vaccination dissent,” Science & Engineering Ethics, Vol. 21, No. 1, 2015, pp. 143–157.

[18] Brian Martin. Evidence-based campaigning. Archives of Public Health, Vol. 76, article 54, 2018, https://doi.org/10.1186/s13690-018-0302-4.

[19] Patrick Stokes, “No, you’re not entitled to your opinion,” The Conversation, 5 October 2012, https://theconversation.com/no-youre-not-entitled-to-your-opinion-9978.

[20] Martin, Vaccination Panic in Australia, 292–304.

[21] W. Lawrence Neuman, Social Research Methods: Qualitative and Quantitative Approaches, seventh edition (Boston, MA: Pearson, 2011), 3–5.

[22] Wayne C. Booth, Gregory G. Colomb, Joseph M. Williams, Joseph Bizup and William T. FitzGerald, The Craft of Research, fourth edition (Chicago: University of Chicago Press, 2016), 272–273.

[23] Malcolm Wright and J. Scott Armstrong, “The ombudsman: verification of citations: fawlty towers of knowledge?” Interfaces, 38 (2), March-April 2008, 125–132.

[24] For a detailed articulation of this approach, see Stephen Hilgartner, “The Sokal affair in context,” Science, Technology, & Human Values, 22(4), Autumn 1997, pp. 506–522. Hilgartner gives numerous citations to expansive interpretations of the significance of the hoax.

[25] See for example Alan D. Sokal, “What the Social Text affair does and does not prove,” in Noretta Koertge (ed.), A House Built on Sand: Exposing Postmodernist Myths about Science (New York: Oxford University Press, 1998), pp. 9–22, at p. 11: “From the mere fact of publication of my parody, I think that not much can be deduced. It doesn’t prove that the whole field of cultural studies, or the cultural studies of science — much less the sociology of science — is nonsense.”

Author information: Moti Mizrahi, Florida Institute of Technology, mmizrahi@fit.edu

Mizrahi, Moti. “More in Defense of Weak Scientism: Another Reply to Brown.” Social Epistemology Review and Reply Collective 7, no. 4 (2018): 7-25.

The pdf of the article gives specific page references. Shortlink: https://wp.me/p1Bfg0-3W1

Please refer to:

Image by eltpics via Flickr / Creative Commons

 

In my (2017a), I defend a view I call Weak Scientism, which is the view that knowledge produced by scientific disciplines is better than knowledge produced by non-scientific disciplines.[1] Scientific knowledge can be said to be quantitatively better than non-scientific knowledge insofar as scientific disciplines produce more impactful knowledge–in the form of scholarly publications–than non-scientific disciplines (as measured by research output and research impact). Scientific knowledge can be said to be qualitatively better than non-scientific knowledge insofar as such knowledge is explanatorily, instrumentally, and predictively more successful than non-scientific knowledge.

Brown (2017a) raises several objections against my defense of Weak Scientism and I have replied to his objections (Mizrahi 2017b), thereby showing again that Weak Scientism is a defensible view. Since then, Brown (2017b) has reiterated his objections in another reply on SERRC. Almost unchanged from his previous attack on Weak Scientism (Brown 2017a), Brown’s (2017b) objections are the following:

  1. Weak Scientism is not strong enough to count as scientism.
  2. Advocates of Strong Scientism should not endorse Weak Scientism.
  3. Weak Scientism does not show that philosophy is useless.
  4. My defense of Weak Scientism appeals to controversial philosophical assumptions.
  5. My defense of Weak Scientism is a philosophical argument.
  6. There is nothing wrong with persuasive definitions of scientism.

In what follows, I will respond to these objections, thereby showing once more that Weak Scientism is a defensible view. Since I have been asked to keep this as short as possible, however, I will try to focus on what I take to be new in Brown’s (2017b) latest attack on Weak Scientism.

Is Weak Scientism Strong Enough to Count as Scientism?

Brown (2017b) argues for (1) on the grounds that, on Weak Scientism, “philosophical knowledge may be nearly as valuable as scientific knowledge.” Brown (2017b, 4) goes on to characterize a view he labels “Scientism2,” which he admits is the same view as Strong Scientism, and says that “there is a huge logical gap between Strong Scientism (Scientism2) and Weak Scientism.”

As was the case the first time Brown raised this objection, it is not clear how it is supposed to show that Weak Scientism is not “really” a (weaker) version of scientism (Mizrahi 2017b, 10-11). Of course there is a logical gap between Strong Scientism and Weak Scientism; that is why I distinguish between these two epistemological views. If I am right, Strong Scientism is too strong to be a defensible version of scientism, whereas Weak Scientism is a defensible (weaker) version of scientism (Mizrahi 2017a, 353-354).

Of course Weak Scientism “leaves open the possibility that there is philosophical knowledge” (Brown 2017b, 5). If I am right, such philosophical knowledge would be inferior to scientific knowledge both quantitatively (in terms of research output and research impact) and qualitatively (in terms of explanatory, instrumental, and predictive success) (Mizrahi 2017a, 358).

Brown (2017b, 5) does try to offer a reason “for thinking it strange that Weak Scientism counts as a species of scientism” in his latest attack on Weak Scientism, which does not appear in his previous attack. He invites us to imagine a theist who believes that “modern science is the greatest new intellectual achievement since the fifteenth century” (emphasis in original). Brown then claims that this theist would be an advocate of Weak Scientism because Brown (2017b, 6) takes “modern science is the greatest new intellectual achievement since the fifteenth century” to be “(roughly) equivalent to Weak Scientism.” For Brown (2017b, 6), however, “it seems odd, to say the least, that [this theist] should count as an advocate (even roughly) of scientism.”

Unfortunately, Brown’s appeal to intuition is rather difficult to evaluate because his hypothetical case is under-described.[2] First, the key phrase, namely, “modern science is the greatest new intellectual achievement since the fifteenth century,” is vague in more ways than one. I have no idea what “greatest” is supposed to mean here. Greatest in what respects? What are the other “intellectual achievements” relative to which science is said to be “the greatest”?

Also, what does “intellectual achievement” mean here? There are multiple accounts and literary traditions in history and philosophy of science, science studies, and the like on what counts as “intellectual achievements” or progress in science (Mizrahi 2013b). Without a clear understanding of what these key phrases mean here, it is difficult to tell how Brown’s intuition about this hypothetical case is supposed to be a reason to think that Weak Scientism is not “really” a (weaker) version of scientism.

Toward the end of his discussion of (1), Brown says something that suggests he actually has an issue with the word ‘scientism’. Brown (2017b, 6) writes, “perhaps Mizrahi should coin a new word for the position with respect to scientific knowledge and non-scientific forms of academic knowledge he wants to talk about” (emphasis in original). It should be clear, of course, that it does not matter what label I use for the view that “Of all the knowledge we have, scientific knowledge is the best knowledge” (Mizrahi 2017a, 354; emphasis in original). What matters is the content of the view, not the label.

Whether Brown likes the label or not, Weak Scientism is a (weaker) version of scientism because it is the view that scientific ways of knowing are superior (in certain relevant respects) to non-scientific ways of knowing, whereas Strong Scientism is the view that scientific ways of knowing are the only ways of knowing. As I have pointed out in my previous reply to Brown, whether scientific ways of knowing are superior to non-scientific ways of knowing is essentially what the scientism debate is all about (Mizrahi 2017b, 13).

Before I conclude this discussion of (1), I would like to point out that Brown seems to have misunderstood Weak Scientism. He (2017b, 3) claims that “Weak Scientism is a normative and not a descriptive claim.” This is a mistake. As a thesis (Peels 2017, 11), Weak Scientism is a descriptive claim about scientific knowledge in comparison to non-scientific knowledge. This should be clear provided that we keep in mind what it means to say that scientific knowledge is better than non-scientific knowledge. As I have argued in my (2017a), to say that scientific knowledge is quantitatively better than non-scientific knowledge is to say that there is a lot more scientific knowledge than non-scientific knowledge (as measured by research output) and that the impact of scientific knowledge is greater than that of non-scientific knowledge (as measured by research impact).

To say that scientific knowledge is qualitatively better than non-scientific knowledge is to say that scientific knowledge is explanatorily, instrumentally, and predictively more successful than non-scientific knowledge. All these claims about the superiority of scientific knowledge to non-scientific knowledge are descriptive, not normative, claims. That is to say, Weak Scientism is the view that, as a matter of fact, knowledge produced by scientific fields of study is quantitatively (in terms of research output and research impact) and qualitatively (in terms of explanatory, instrumental, and predictive success) better than knowledge produced by non-scientific fields of study.

Of course, Weak Scientism does have some normative implications. For instance, if scientific knowledge is indeed better than non-scientific knowledge, then, other things being equal, we should give more evidential weight to scientific knowledge than to non-scientific knowledge. For example, suppose that I am considering whether to vaccinate my child or not. On the one hand, I have scientific knowledge in the form of results from clinical trials according to which MMR vaccines are generally safe and effective.

On the other hand, I have knowledge in the form of stories about children who were vaccinated and then began to display symptoms of autism. If Weak Scientism is true, and I want to make a decision based on the best available information, then I should give more evidential weight to the scientific knowledge about MMR vaccines than to the anecdotal knowledge about MMR vaccines simply because the former is scientific (i.e., knowledge obtained by means of the methods of science, such as clinical trials) and the latter is not.

Should Advocates of Strong Scientism Endorse Weak Scientism?

Brown (2017b, 7) argues for (2) on the grounds that “once the advocate of Strong Scientism sees that an advocate of Weak Scientism admits the possibility that there is real knowledge other than what is produced by the natural sciences […] the advocate of Strong Scientism, at least given their philosophical presuppositions, will reject Weak Scientism out of hand.” It is not clear which “philosophical presuppositions” Brown is talking about here. Brown quotes Rosenberg (2011, 20), who claims that physics tells us what reality is like, presumably as an example of a proponent of Strong Scientism who would not endorse Weak Scientism. But it is not clear why Brown thinks that Rosenberg would “reject Weak Scientism out of hand” (Brown 2017d, 7).

Like other proponents of scientism, Rosenberg should endorse Weak Scientism because, unlike Strong Scientism, Weak Scientism is a defensible view. Insofar as we should endorse the view that has the most evidence in its favor, Weak Scientism has more going for it than Strong Scientism does. For to show that Strong Scientism is true, one would have to show that no field of study other than scientific ones can produce knowledge. Of course, that is not easy to show. To show that Weak Scientism is true, one only needs to show that the knowledge produced in scientific fields of study is better (in certain relevant respects) than the knowledge produced in non-scientific fields.

That is precisely what I show in my (2017a). I argue that the knowledge produced in scientific fields is quantitatively better than the knowledge produced in non-scientific fields because there is a lot more scientific knowledge than non-scientific knowledge (as measured by research output) and the former has a greater impact than the latter (as measured by research impact). I also argue that the knowledge produced in scientific fields is qualitatively better than knowledge produced in non-scientific fields because it is more explanatorily, instrumentally, and predictively successful.

Contrary to what Brown (2017b, 7) seems to think, I do not have to show “that there is real knowledge other than scientific knowledge.” To defend Weak Scientism, all I have to show is that scientific knowledge is better (in certain relevant respects) than non-scientific knowledge. If anyone must argue for the claim that there is real knowledge other than scientific knowledge, it is Brown, for he wants to defend the value or usefulness of non-scientific knowledge, specifically, philosophical knowledge.

It is important to emphasize the point about the ways in which scientific knowledge is quantitatively and qualitatively better than non-scientific knowledge because it looks like Brown has confused the two. For he thinks that I justify my quantitative analysis of scholarly publications in scientific and non-scientific fields by “citing the precedent of epistemologists who often treat all items of knowledge as qualitatively the same” (Brown 2017b, 22; emphasis added).

Here Brown fails to carefully distinguish between my claim that scientific knowledge is quantitatively better than non-scientific knowledge and my claim that scientific knowledge is qualitatively better than non-scientific knowledge. For the purposes of a quantitative study of knowledge, information and data scientists can do precisely what epistemologists do and “abstract from various circumstances (by employing variables)” (Brown 2017b, 22) in order to determine which knowledge is quantitatively better.

How Is Weak Scientism Relevant to the Claim that Philosophy Is Useless?

Brown (2017b, 7-8) argues for (3) on the grounds that “Weak Scientism itself implies nothing about the degree to which philosophical knowledge is valuable or useful other than stating scientific knowledge is better than philosophical knowledge” (emphasis in original).

Strictly speaking, Brown is wrong about this because Weak Scientism does imply something about the degree to which scientific knowledge is better than philosophical knowledge. Recall that to say that scientific knowledge is quantitatively better than non-scientific knowledge is to say that scientific fields of study publish more research and that scientific research has greater impact than the research published in non-scientific fields of study.

Contrary to what Brown seems to think, we can say to what degree scientific research is superior to non-scientific research in terms of output and impact. That is precisely what bibliometric indicators like h-index and other metrics are for (Rousseau et al. 2018). Such bibliometric indicators allow us to say how many articles are published in a given field, how many of those published articles are cited, and how many times they are cited. For instance, according to Scimago Journal & Country Rank (2018), which contains data from the Scopus database, of the 3,815 Philosophy articles published in the United States in 2016-2017, approximately 14% are cited, and their h-index is approximately 160.

On the other hand, of the 24,378 Psychology articles published in the United States in 2016-2017, approximately 40% are cited, and their h-index is approximately 640. Contrary to what Brown seems to think, then, we can say to what degree research in Psychology is better than research in Philosophy in terms of research output (i.e., number of publications) and research impact (i.e., number of citations). We can use the same bibliometric indicators and metrics to compare research in other scientific and non-scientific fields of study.

As I have already said in my previous reply to Brown, “Weak Scientism does not entail that philosophy is useless” and “I have no interest in defending the charge that philosophy is useless” (Mizrahi 2017b, 11-12). So, I am not sure why Brown brings up (3) again. Since he insists, however, let me explain why philosophers who are concerned about the charge that philosophy is useless should engage with Weak Scientism as well.

Suppose that a foundation or agency is considering whether to give a substantial grant to one of two projects. The first project is that of a philosopher who will sit in her armchair and contemplate the nature of friendship.[3] The second project is that of a team of social scientists who will conduct a longitudinal study of the effects of friendship on human well-being (e.g., Yang et al. 2016).

If Weak Scientism is true, and the foundation or agency wants to fund the project that is likely to yield better results, then it should give the grant to the team of social scientists rather than to the armchair philosopher simply because the former’s project is scientific, whereas the latter’s is not. This is because the scientific project will more likely yield better knowledge than the non-scientific project will. In other words, unlike the project of the armchair philosopher, the scientific project will probably produce more research (i.e., more publications) that will have a greater impact (i.e., more citations) and the knowledge produced will be explanatorily, instrumentally, and predictively more successful than any knowledge that the philosopher’s project might produce.

This example should really hit home for Brown, since reading his latest attack on Weak Scientism gives one the impression that he thinks of philosophy as a personal, “self-improvement” kind of enterprise, rather than an academic discipline or field of study. For instance, he seems to be saying that philosophy is not in the business of producing “new knowledge” or making “discoveries” (Brown 2017b, 17).

Rather, Brown (2017b, 18) suggests that philosophy “is more about individual intellectual progress rather than collective intellectual progress.” Individual progress or self-improvement is great, of course, but I am not sure that it helps Brown’s case in defense of philosophy against what he sees as “the menace of scientism.” For this line of thinking simply adds fuel to the fire set by those who want to see philosophy burn. As I point out in my (2017a), scientists who dismiss philosophy do so because they find it academically useless.

For instance, Hawking and Mlodinow (2010, 5) write that ‘philosophy is dead’ because it ‘has not kept up with developments in science, particularly physics’ (emphasis added). Similarly, Weinberg (1994, 168) says that, as a working scientist, he ‘finds no help in professional philosophy’ (emphasis added). (Mizrahi 2017a, 356)

Likewise, Richard Feynman is rumored to have said that “philosophy of science is about as useful to scientists as ornithology is to birds” (Kitcher 1998, 32). It is clear, then, that what these scientists complain about is professional or academic philosophy. Accordingly, they would have no problem with anyone who wants to pursue philosophy for the sake of “individual intellectual progress.” But that is not the issue here. Rather, the issue is academic knowledge or research.

Does My Defense of Weak Scientism Appeal to Controversial Philosophical Assumptions?

Brown (2017b, 9) argues for (4) on the grounds that I assume that “we are supposed to privilege empirical (I read Mizrahi’s ‘empirical’ here as ‘experimental/scientific’) evidence over non-empirical evidence.” But that is question-begging, Brown claims, since he takes me to be assuming something like the following: “If the question of whether scientific knowledge is superior to [academic] non-scientific knowledge is a question that one can answer empirically, then, in order to pose a serious challenge to my [Mizrahi’s] defense of Weak Scientism, Brown must come up with more than mere ‘what ifs’” (Mizrahi 2017b, 10; quoted in Brown 2017b, 8).

This objection seems to involve a confusion about how defeasible reasoning and defeating evidence are supposed to work. Given that “a rebutting defeater is evidence which prevents E from justifying belief in H by supporting not-H in a more direct way” (Kelly 2016), claims about what is actual cannot be defeated by mere possibilities, since claims of the form “Possibly, p” do not prevent a piece of evidence from justifying belief in “Actually, p” by supporting “Actually, not-p” directly.

For example, the claim “Hillary Clinton could have been the 45th President of the United States” does not prevent my perceptual and testimonial evidence from justifying my belief in “Donald Trump is the 45th President of the United States,” since the former does not support “It is not the case that Donald Trump is the 45th President of the United States” in a direct way. In general, claims of the form “Possibly, p” are not rebutting defeaters against claims of the form “Actually, p.” Defeating evidence against claims of the form “Actually, p” must be about what is actual (or at least probable), not what is merely possible, in order to support “Actually, not-p” directly.

For this reason, although “the production of some sorts of non-scientific knowledge work may be harder than the production of scientific knowledge” (Brown 2017b, 19), Brown gives no reasons to think that it is actually or probably harder, which is why this possibility does nothing to undermine the claim that scientific knowledge is actually better than non-scientific knowledge. Just as it is possible that philosophical knowledge is harder to produce than scientific knowledge, it is also possible that scientific knowledge is harder to produce than philosophical knowledge. It is also possible that scientific and non-scientific knowledge are equally hard to produce.

Similarly, the possibility that “a little knowledge about the noblest things is more desirable than a lot of knowledge about less noble things” (Brown 2017b, 19), whatever “noble” is supposed to mean here, does not prevent my bibliometric evidence (in terms of research output and research impact) from justifying the belief that scientific knowledge is better than non-scientific knowledge. Just as it is possible that philosophical knowledge is “nobler” (whatever that means) than scientific knowledge, it is also possible that scientific knowledge is “nobler” than philosophical knowledge or that they are equally “noble” (Mizrahi 2017b, 9-10).

In fact, even if Brown (2017a, 47) is right that “philosophy is harder than science” and that “knowing something about human persons–particularly qua embodied rational being–is a nobler piece of knowledge than knowing something about any non-rational object” (Brown 2017b, 21), whatever “noble” is supposed to mean here, it would still be the case that scientific fields produce more knowledge (as measured by research output), and more impactful knowledge (as measured by research impact), than non-scientific disciplines.

So, I am not sure why Brown keeps insisting on mentioning these mere possibilities. He also seems to forget that the natural and social sciences study human persons as well. Even if knowledge about human persons is “nobler” (whatever that means), there is a lot of scientific knowledge about human persons coming from scientific fields, such as anthropology, biology, genetics, medical science, neuroscience, physiology, psychology, and sociology, to name just a few.

One of the alleged “controversial philosophical assumptions” that my defense of Weak Scientism rests on, and that Brown (2017a) complains about the most in his previous attack on Weak Scientism, is my characterization of philosophy as the scholarly work that professional philosophers do. In my previous reply, I argue that Brown is not in a position to complain that this is a “controversial philosophical assumption,” since he rejects my characterization of philosophy as the scholarly work that professional philosophers produce, but he does not tell us what counts as philosophical (Mizrahi 2017b, 13). Well, it turns out that Brown does not reject my characterization of philosophy after all. For, after he was challenged to say what counts as philosophical, he came up with the following “sufficient condition for pieces of writing and discourse that count as philosophy” (Brown 2017b, 11):

(P) Those articles published in philosophical journals and what academics with a Ph.D. in philosophy teach in courses at public universities with titles such as Introduction to Philosophy, Metaphysics, Epistemology, Normative Ethics, and Philosophy of Science (Brown 2017b, 11; emphasis added).

Clearly, this is my characterization of philosophy in terms of the scholarly work that professional philosophers produce. Brown simply adds teaching to it. Since he admits that “scientists teach students too” (Brown 2017b, 18), however, it is not clear how adding teaching to my characterization of philosophy is supposed to support his attack on Weak Scientism. In fact, it may actually undermine his attack on Weak Scientism, since there is a lot more teaching going on in STEM fields than in non-STEM fields.

According to data from the National Center for Education Statistics (2017), in the 2015-16 academic year, post-secondary institutions in the United States conferred only 10,157 Bachelor’s degrees in philosophy and religious studies compared to 113,749 Bachelor’s degrees in biological and biomedical sciences, 106,850 Bachelor’s degrees in engineering, and 117,440 in psychology. In general, in the 2015-2016 academic year, 53.3% of the Bachelor’s degrees conferred by post-secondary institutions in the United States were degrees in STEM fields, whereas only 5.5% of conferred Bachelor’s degrees were in the humanities (Figure 1).

Figure 1. Bachelor’s degrees conferred by post-secondary institutions in the US, by field of study, 2015-2016 (Source: NCES)

 

Clearly, then, there is a lot more teaching going on in science than in philosophy (or even in the humanities in general), since a lot more students take science courses and graduate with degrees in scientific fields of study. So, even if Brown is right that we should include teaching in what counts as philosophy, it is still the case that scientific fields are quantitatively better than non-scientific fields.

Since Brown (2017b, 13) seems to agree that philosophy (at least in part) is the scholarly work that academic philosophers produce, it is peculiar that he complains, without argument, that “an understanding of philosophy and knowledge as operational is […] shallow insofar as philosophy and knowledge can’t fit into the narrow parameters of another empirical study.” Once Brown (2017b, 11) grants that “Those articles published in philosophical journals” count as philosophy, he thereby also grants that these journal articles can be studied empirically using the methods of bibliometrics, information science, or data science.

That is, Brown (2017b, 11) concedes that philosophy consists (at least in part) of “articles published in philosophical journals,” and so these articles can be compared to other articles published in science journals to determine research output, and they can also be compared to articles published in science journals in terms of citation counts to determine research impact. What exactly is “shallow” about that? Brown does not say.

A, perhaps unintended, consequence of Brown’s (P) is that the “great thinkers from the past” (Brown 2017b, 18), those that Brown (2017b, 13) likes to remind us “were not professional philosophers,” did not do philosophy, by Brown’s own lights. For “Socrates, Plato, Augustine, Descartes, Locke, and Hume” (Brown 2017b, 13) did not publish in philosophy journals, were not academics with a Ph.D. in philosophy, and did not teach at public universities courses “with titles such as Introduction to Philosophy, Metaphysics, Epistemology, Normative Ethics, and Philosophy of Science” (Brown 2017b, 11).

Another peculiar thing about Brown’s (P) is the restriction of the philosophical to what is being taught in public universities. What about community colleges and private universities? Is Brown suggesting that philosophy courses taught at private universities do not count as philosophy courses? This is peculiar, especially in light of the fact that, at least according to The Philosophical Gourmet Report (Brogaard and Pynes 2018), the top ranked philosophy programs in the United States are mostly located in private universities, such as New York University and Princeton University.

Is My Defense of Weak Scientism a Scientific or a Philosophical Argument?

Brown argues for (5) on the grounds that my (2017a) is published in a philosophy journal, namely, Social Epistemology, and so it a piece of philosophical knowledge by my lights, since I count as philosophy the research articles that are published in philosophy journals.

Brown would be correct about this if Social Epistemology were a philosophy journal. But it is not. Social Epistemology: A Journal of Knowledge, Culture and Policy is an interdisciplinary journal. The journal’s “aim and scope” statement makes it clear that Social Epistemology is an interdisciplinary journal:

Social Epistemology provides a forum for philosophical and social scientific enquiry that incorporates the work of scholars from a variety of disciplines who share a concern with the production, assessment and validation of knowledge. The journal covers both empirical research into the origination and transmission of knowledge and normative considerations which arise as such research is implemented, serving as a guide for directing contemporary knowledge enterprises (Social Epistemology 2018).

The fact that Social Epistemology is an interdisciplinary journal, with contributions from “Philosophers, sociologists, psychologists, cultural historians, social studies of science researchers, [and] educators” (Social Epistemology 2018) would not surprise anyone who is familiar with the history of the journal. The founding editor of the journal is Steve Fuller, who was trained in an interdisciplinary field, namely, History and Philosophy of Science (HPS), and is currently the Auguste Comte Chair in Social Epistemology in the Department of Sociology at Warwick University. Brown (2017b, 15) would surely agree that sociology is not philosophy, given that, for him, “cataloguing what a certain group of people believes is sociology and not philosophy.” The current executive editor of the journal is James H. Collier, who is a professor of Science and Technology in Society at Virginia Tech, and who was trained in Science and Technology Studies (STS), which is an interdisciplinary field as well.

Brown asserts without argument that the methods of a scientific field of study, such as sociology, are different in kind from those of philosophy: “What I contend is that […] philosophical methods are different in kind from those of the experimental scientists [sciences?]” (Brown 2017b, 24). He then goes on to speculate about what it means to say that an explanation is testable (Brown 2017b, 25). What Brown comes up with is rather unclear to me. For instance, I have no idea what it means to evaluate an explanation by inductive generalization (Brown 2017b, 25).

Instead, Brown should have consulted any one of the logic and reasoning textbooks I keep referring to in my (2017a) and (2017b) to find out that it is generally accepted among philosophers that the good-making properties of explanations, philosophical and otherwise, include testability among other good-making properties (see, e.g., Sinnott-Armstrong and Fogelin 2010, 257). As far as testability is concerned, to test an explanation or hypothesis is to determine “whether predictions that follow from it are true” (Salmon 2013, 255). In other words, “To say that a hypothesis is testable is at least to say that some prediction made on the basis of that hypothesis may confirm or disconfirm it” (Copi et al. 2011, 515).

For this reason, Feser’s analogy according to which “to compare the epistemic values of science and philosophy and fault philosophy for not being good at making testable predications [sic] is like comparing metal detectors and gardening tools and concluding gardening tools are not as good as metal detectors because gardening tools do not allow us to successfully detect for metal” (Brown 2017b, 25), which Brown likes to refer to (Brown 2017a, 48), is inapt.

It is not an apt analogy because, unlike metal detectors and gardening tools, which serve different purposes, both science and philosophy are in the business of explaining things. Indeed, Brown admits that, like good scientific explanations, “good philosophical theories explain things” (emphasis in original). In other words, Brown admits that both scientific and philosophical theories are instruments of explanation (unlike gardening and metal-detecting instruments). To provide good explanations, then, both scientific and philosophical theories must be testable (Mizrahi 2017b, 19-20).

What Is Wrong with Persuasive Definitions of Scientism?

Brown (2017b, 31) argues for (6) on the grounds that “persuasive definitions are [not] always dialectically pernicious.” He offers an argument whose conclusion is “abortion is murder” as an example of an argument for a persuasive definition of abortion. He then outlines an argument for a persuasive definition of scientism according to which “Weak Scientism is a view that has its advocates putting too high a value on scientific knowledge” (Brown 2017b, 32).

The problem, however, is that Brown is confounding arguments for a definition with the definition itself. Having an argument for a persuasive definition does not change the fact that it is a persuasive definition. To illustrate this point, let me give an example that I think Brown will appreciate. Suppose I define theism as an irrational belief in the existence of God. That is, “theism” means “an irrational belief in the existence of God.” I can also provide an argument for this definition:

P1: If it is irrational to have paradoxical beliefs and God is a paradoxical being, then theism is an irrational belief in the existence of God.

P2: It is irrational to have paradoxical beliefs and God is a paradoxical being (e.g., the omnipotence paradox).[4]

Therefore,

C: Theism is an irrational belief in the existence of God.

But surely, theists will complain that my definition of theism is a “dialectically pernicious” persuasive definition. For it stacks the deck against theists. It states that theists are already making a mistake, by definition, simply by believing in the existence of God. Even though I have provided an argument for this persuasive definition of theism, my definition is still a persuasive definition of theism, and my argument is unlikely to convince anyone who doesn’t already think that theism is irrational. Indeed, Brown (2017b, 30) himself admits that much when he says “good luck with that project!” about trying to construct a sound argument for “abortion is murder.” I take this to mean that pro-choice advocates would find his argument for “abortion is murder” dialectically inert precisely because it defines abortion in a manner that transfers “emotive force” (Salmon 2013, 65), which they cannot accept.

Likewise, theists would find the argument above dialectically inert precisely because it defines theism in a manner that transfers “emotive force” (Salmon 2013, 65), which they cannot accept. In other words, Brown seems to agree that there are good dialectical reasons to avoid appealing to persuasive definitions. Therefore, like “abortion is murder,” “theism is an irrational belief in the existence of God,” and “‘Homosexual’ means ‘one who has an unnatural desire for those of the same sex’” (Salmon 2013, 65), “Weak Scientism is a view that has its advocates putting too high a value on scientific knowledge” (Brown 2017b, 32) is a “dialectically pernicious” persuasive definition (cf. Williams 2015, 14).

Like persuasive definitions in general, it “masquerades as an honest assignment of meaning to a term while condemning or blessing with approval the subject matter of the definiendum” (Hurley 2015, 101). As I have pointed out in my (2017a), the problem with such definitions is that they “are strategies consisting in presupposing an unaccepted definition, taking a new unknowable description of meaning as if it were commonly shared” (Macagno and Walton 2014, 205).

As for Brown’s argument for the persuasive definition of Weak Scientism, according to which it “is a view that has its advocates putting too high a value on scientific knowledge” (Brown 2017b, 32), a key premise in this argument is the claim that there is a piece of philosophical knowledge that is better than scientific knowledge. This is premise 36 in Brown’s argument:

Some philosophers qua philosophers know that (a) true friendship is a necessary condition for human flourishing and (b) the possession of the moral virtues or a life project aimed at developing the moral virtues is a necessary condition for true friendship and (c) (therefore) the possession of the moral virtues or a life project aimed at developing the moral virtues is a necessary condition for human flourishing (see, e.g., the arguments in Plato’s Gorgias) and knowledge concerning the necessary conditions of human flourishing is better than any sort of scientific knowledge (see, e.g., St. Augustine’s Confessions, book five, chapters iii and iv) [assumption]

There is a lot to unpack here, but I will focus on what I take to be the points most relevant to the scientism debate. First, Brown assumes 36 without argument, but why think it is true? In particular, why think that (a), (b), and (c) count as philosophical knowledge? Brown says that philosophers know (a), (b), and (c) in virtue of being philosophers, but he does not tell us why that is the case.

After all, accounts of friendship, with lessons about the significance of friendship, predate philosophy (see, e.g., the friendship of Gilgamesh and Enkidu in The Epic of Gilgamesh). Did it really take Plato and Augustine to tell us about the significance of friendship? In fact, on Brown’s characterization of philosophy, namely, (P), (a), (b), and (c) do not count as philosophical knowledge at all, since Plato and Augustine did not publish in philosophy journals, were not academics with a Ph.D. in philosophy, and did not teach at public universities courses “with titles such as Introduction to Philosophy, Metaphysics, Epistemology, Normative Ethics, and Philosophy of Science” (Brown 2017b, 11).

Second, some philosophers, like Epicurus, need (and think that others need) friends to flourish, whereas others, like Diogenes of Sinope, need no one. For Diogenes, friends will only interrupt his sunbathing (Arrian VII.2). My point is not simply that philosophers disagree about the value of friendship and human flourishing. Of course they disagree.[5]

Rather, my point is that, in order to establish general truths about human beings, such as “Human beings need friends to flourish,” one must employ the methods of science, such as randomization and sampling procedures, blinding protocols, methods of statistical analysis, and the like; otherwise, one would simply commit the fallacies of cherry-picking anecdotal evidence and hasty generalization (Salmon 2013, 149-151). After all, the claim “Some need friends to flourish” does not necessitate, or even make more probable, the truth of “Human beings need friends to flourish.”[6]

Third, why think that “knowledge concerning the necessary conditions of human flourishing is better than any sort of scientific knowledge” (Brown 2017b, 32)? Better in what sense? Quantitatively? Qualitatively? Brown does not tell us. He simply declares it “self-evident” (Brown 2017b, 32). I take it that Brown would not want to argue that “knowledge concerning the necessary conditions of human flourishing” is better than scientific knowledge in the quantitative (i.e., in terms of research output and research impact) and qualitative (i.e., in terms of explanatory, instrumental, and predictive success) respects in which scientific knowledge is better than non-scientific knowledge, according to Weak Scientism.

If so, then in what sense exactly “knowledge concerning the necessary conditions of human flourishing” (Brown 2017b, 32) is supposed to be better than scientific knowledge? Brown (2017b, 32) simply assumes that without argument and without telling us in what sense exactly “knowledge concerning the necessary conditions of human flourishing is better than any sort of scientific knowledge” (Brown 2017b, 32).

Of course, philosophy does not have a monopoly on friendship and human flourishing as research topics. Psychologists and sociologists, among other scientists, work on friendship as well (see, e.g., Hojjat and Moyer 2017). To get an idea of how much research on friendship is done in scientific fields, such as psychology and sociology, and how much is done in philosophy, we can use a database like Web of Science.

Currently (03/29/2018), there are 12,334 records in Web of Science on the topic “friendship.” Only 76 of these records (0.61%) are from the Philosophy research area. Most of the records are from the Psychology (5,331 records) and Sociology (1,111) research areas (43.22% and 9%, respectively). As we can see from Figure 2, most of the research on friendship is done in scientific fields of study, such as psychology, sociology, and other social sciences.

Figure 2. Number of records on the topic “friendship” in Web of Science by research area (Source: Web of Science)

 

In terms of research impact, too, scientific knowledge about friendship is superior to philosophical knowledge about friendship. According to Web of Science, the average citations per year for Psychology research articles on the topic of friendship is 2826.11 (h-index is 148 and the average citations per item is 28.1), and the average citations per year for Sociology research articles on the topic of friendship is 644.10 (h-index is 86 and the average citations per item is 30.15), whereas the average citations per year for Philosophy research articles on friendship is 15.02 (h-index is 13 and the average citations per item is 8.11).

Quantitatively, then, psychological and sociological knowledge on friendship is better than philosophical knowledge in terms of research output and research impact. Both Psychology and Sociology produce significantly more research on friendship than Philosophy does, and the research they produce has significantly more impact (as measured by citation counts) than philosophical research on the same topic.

Qualitatively, too, psychological and sociological knowledge about friendship is better than philosophical knowledge about friendship. For, instead of rather vague statements about how “true friendship is a necessary condition for human flourishing” (Brown 2017b, 32) that are based on mostly armchair speculation, psychological and sociological research on friendship provides detailed explanations and accurate predictions about the effects of friendship (or lack thereof) on human well-being.

For instance, numerous studies provide evidence for the effects of friendships or lack of friendships on physical well-being (see, e.g., Yang et al. 2016) as well as mental well-being (see, e.g., Cacioppo and Patrick 2008). Further studies provide explanations for the biological and genetic bases of these effects (Cole et al. 2011). This knowledge, in turn, informs interventions designed to help people deal with loneliness and social isolation (see, e.g., Masi et al. 2010).[7]

To sum up, Brown (2017b, 32) has given no reasons to think that “knowledge concerning the necessary conditions of human flourishing is better than any sort of scientific knowledge.” He does not even tell us what “better” is supposed to mean here. He also ignores the fact that scientific fields of study, such as psychology and sociology, produce plenty of knowledge about human flourishing, both physical and mental well-being. In fact, as we have seen, science produces a lot more knowledge about topics related to human well-being, such as friendship, than philosophy does. For this reason, Brown (2017b, 32) has failed to show that “there is non-scientific form of knowledge better than scientific knowledge.”

Conclusion

At this point, I think it is quite clear that Brown and I are talking past each other on a couple of levels. First, I follow scientists (e.g., Weinberg 1994, 166-190) and philosophers (e.g., Haack 2007, 17-18 and Peels 2016, 2462) on both sides of the scientism debate in treating philosophy as an academic discipline or field of study, whereas Brown (2017b, 18) insists on thinking about philosophy as a personal activity of “individual intellectual progress.” Second, I follow scientists (e.g., Hawking and Mlodinow 2010, 5) and philosophers (e.g., Kidd 2016, 12-13 and Rosenberg 2011, 307) on both sides of the scientism debate in thinking about knowledge as the scholarly work or research produced in scientific fields of study, such as the natural sciences, as opposed to non-scientific fields of study, such as the humanities, whereas Brown insists on thinking about philosophical knowledge as personal knowledge.

To anyone who wishes to defend philosophy’s place in research universities alongside academic disciplines, such as history, linguistics, and physics, armed with this conception of philosophy as a “self-improvement” activity, I would use Brown’s (2017b, 30) words to say, “good luck with that project!” A much more promising strategy, I propose, is for philosophy to embrace scientific ways of knowing and for philosophers to incorporate scientific methods into their research.[8]

Contact details: mmizrahi@fit.edu

References

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Macagno, F., and D. Walton. Emotive Language in Argumentation. New York: Cambridge University Press, 2014.

Masi, C. M., H. Chen, and L. C. Hawkley. “A Meta-Analysis of Interventions to Reduce Loneliness.” Personality and Social Psychology Review 15, no. 3 (2011): 219-266.

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Mizrahi, M. “More Intuition Mongering.” The Reasoner 7, no. 1 (2013a): 5-6.

Mizrahi, M. “What is Scientific Progress? Lessons from Scientific Practice.” Journal for General Philosophy of Science 44, no. 2 (2013b): 375-390.

Mizrahi, M. “New Puzzles about Divine Attributes.” European Journal for Philosophy of Religion 5, no. 2 (2013c): 147-157.

Mizrahi, M. “The Pessimistic Induction: A Bad Argument Gone Too Far.” Synthese 190, no. 15 (2013d): 3209-3226.

Mizrahi, M. “Does the Method of Cases Rest on a Mistake?” Review of Philosophy and Psychology 5, no. 2 (2014): 183-197.

Mizrahi, M. “On Appeals to Intuition: A Reply to Muñoz-Suárez.” The Reasoner 9, no. 2 (2015a): 12-13.

Mizrahi, M. “Don’t Believe the Hype: Why Should Philosophical Theories Yield to Intuitions?” Teorema: International Journal of Philosophy 34, no. 3 (2015b): 141-158.

Mizrahi, M. “Historical Inductions: New Cherries, Same Old Cherry-Picking.” International Studies in the Philosophy of Science 29, no. 2 (2015c): 129-148.

Mizrahi, M. “Three Arguments against the Expertise Defense.” Metaphilosophy 46, no. 1 (2015d): 52-64.

Mizrahi, M. “The History of Science as a Graveyard of Theories: A Philosophers’ Myth?” International Studies in the Philosophy of Science 30, no. 3 (2016): 263-278.

Mizrahi, M. “What’s So Bad about Scientism?” Social Epistemology 31, no. 4 (2017a): 351-367.

Mizrahi, M. “In Defense of Weak Scientism: A Reply to Brown.” Social Epistemology Review and Reply Collective 6, no. 11 (2017b): 9-22.

Mizrahi, M. “Introduction.” In The Kuhnian Image of Science: Time for a Decisive Transformation? Edited by M. Mizrahi, 1-22. London: Rowman & Littlefield, 2017c.

National Center for Education Statistics. “Bachelor’s degrees conferred by postsecondary institutions, by field of study: Selected years, 1970-71 through 2015-16.” Digest of Education Statistics (2017). https://nces.ed.gov/programs/digest/d17/tables/dt17_322.10.asp?current=yes.

Peels, R. “The Empirical Case Against Introspection.” Philosophical Studies 17, no. 9 (2016): 2461-2485.

Peels, R. “Ten Reasons to Embrace Scientism.” Studies in History and Philosophy of Science Part A 63 (2017): 11-21.

Rosenberg, A. The Atheist’s Guide to Reality: Enjoying Life Without Illusions. New York: W. W. Norton, 2011.

Rousseau, R., L. Egghe, and R. Guns. Becoming Metric-Wise: A Bibliometric Guide for Researchers. Cambridge, MA: Elsevier, 2018.

Salmon, M. H. Introduction to Logic and Critical Thinking. Sixth Edition. Boston, MA: Wadsworth, 2013.

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[1] I thank Adam Riggio for inviting me to respond to Brown’s second attack on Weak Scientism.

[2] On why appeals to intuition are bad arguments, see Mizrahi (2012), (2013a), (2014), (2015a), (2015b), and (2015d).

[3] I use friendship as an example here because Brown (2017b, 31) uses it as an example of philosophical knowledge. I will say more about that in Section 6.

[4] For more on paradoxes involving the divine attributes, see Mizrahi (2013c).

[5] “Friendship is unnecessary, like philosophy, like art, like the universe itself (for God did not need to create)” (Lewis 1960, 71).

[6] On fallacious inductive reasoning in philosophy, see Mizrahi (2013d), (2015c), (2016), and (2017c).

[7] See also “The Friendship Bench” project: https://www.friendshipbenchzimbabwe.org/.

[8] For recent examples, see Ashton and Mizrahi (2017) and (2018).

Author Information: Gabriel Vélez-Cuartas, Universidad de Antioquia, gjaime.velez@udea.edu.co

Vélez-Cuartas, Gabriel. “Invisible Colleges 2.0: Eponymy as a Scientometric Tool.” Social Epistemology Review and Reply Collective 7, no. 3 (2018): 5-8.

Please refer to:

The pdf of the article gives specific page references. Shortlink: https://wp.me/p1Bfg0-3Vd

The corridors of an invisible college. Image from Justin Kern via Flickr / Creative Commons

 

Merton’s idea of eponymy as a prize for scientists, perhaps the most great of incentives, relatively addressed for a few ones, is revisited in the text from Collazo et al. An idea exposed nearly as a footnote in Merton’s Sociology of Science let open in this text two ideas that can be amplified as opportunities to go a step further in understanding scientific dynamics: (1) The idea of a literary figure as catalyzer of cognitive evolution of scientific communities; (2) the claims for geographical priority to show relevance in the hierarchy of science structures.

Faculty of the Invisible Colleges

(1) Derek de Solla Price (1963) and Diane Crane (1972) developed in the sixties and seventies of the last century the idea of invisible colleges. Those invisible colleges merged the idea of scientific growth due to chained interactions that made possible diffusion of innovations in cycles of exponential and linear growth. This statistic idea of growth has been related to the idea of paradigmatic revolutions in Kuhn’s ideas. These interactions determined the idea of a cognitive dynamic expressed in networks of papers linked by common references in Crane and De Solla Price. In other words, knowledge growth is possible because there are forms of interactions that make possible the construction of communities.

This idea has not evolved in time and appears in different works as: institutionalized communities combining co-authorship networks and citation indexes (Kretschermer 1994), social networks of supervisors, students and co-workers (Verspagen and Werker 2003; Brunn and O’Lear 1999; cultural circles (Chubin 1985); collaboration networks and preferential attachment (Verspagen and Werker 2004; Zuccala 2006).

More recently, the cognitive dynamic related to the other side of the definition of invisible colleges have been some advances focused on detecting cognitive communities. For instance, studies of bibliographic coupling based on similarity algorithms (Leydesdorff 2008; Colliander and Ahlgren 2012; Steinert and Hoppe 2017; Ciotti et al. 2016); hybrid techniques mixing different similarity measures, modularity procedures, and text- and citation-based analysis (Glänzel and Thijs 2017); and the explicit merge made by Van Raan (2014), he proposes a bibliometric analysis mixing co-word analysis, co-citation, and bibliographic coupling to describe invisible colleges dynamics.

Those advances in analysis claim for a transformation of the concept of invisible colleges. The determination of cognitive dynamics by interactions is on the shell. Indeed, different levels of hierarchies and determinations in multilayer networks are arising. This means that collaboration networks can be seen as local interactions embedded in a more global set of relationships shaped by all kind of scientific communications chained in networks of references (Luhmann, 1996).

Eponymy in scientific communication gives a sign of these dynamics. We agree that in the first level of interactions eponymy can describe prestige dynamics, accumulation of social or scientific capital as Bourdieu can describe in his theory of fields. Nevertheless, in a global context of the scientific system, Eponymy acts as a code that catalyzes communication functions in the scientific production. Different programs emerge from the mention of Jerzy Plebanski in the literature (the eponym analyzed within the text from Collazo et al), nevertheless is a common sign for all this communities. The eponymy gives a kind of confidence, content to be trusted and the scientific small masses confirm that by the grace of redundancy. Prestige becomes a communication function, more important than a guide for address the interaction.

How the Eponym Stakes an Invisible College’s Claim

(2) In this direction, the eponym appears as a rhetoric strategy in a semantic context of a determined scientific area, a partial system within the scientific form to communicate debates, controversies and research results. The geographical issue disappears in a way for this system. Cognitively, Jerzy Plebanski is a physicist; a geographical claim for the contributions seems distant to the discussion about the formation of invisible colleges or scientific communities.

Nevertheless, there are two underlying dynamics related to the space as category. One is the outlined dynamic of diffusion of knowledge. The eponym made itself stronger as a figure as can be redundant in many places. Diffusion is related here with dispersion. The strength of eponymy is due to the reach of dispersion that have emerged from redundancy of his name in different global spaces. It means penetration too.

The second is that scientific communities are locally situated and they are possible due to an economic and political context. It can be said that a scientific system needs roots on contexts that facilitate a scientific ethos. The modern expansion through colonies around the world left as a legacy the scientific way as a social function installed in almost every culture. But the different levels of institutional development affect the formation of local scientific communities conditioned by: the struggle between economic models based or non-based on scientific and technological knowledge (Arocena & Sutz, 2013); cultural coloniality (Quijano, 2007); the openness of science and the concentration of knowledge in private companies as part of a regime of intellectual property (Vélez Cuartas et al, 2018).

In other words, the claim for the work of Jerzy Plebanski as a Mexican and the appearance of eponym in Latin American lands borne as an exclamation. The acknowledgement of Latin American science is a kind of reaffirmation. In logic of scientific system observed from the Global North it seems a trivial issue, where a dictionary of scientific eponyms can list more than 9,000 renamed scientists. The geographical issue plays in two sides to comprehend this dynamic: from one side, the penetration of a global scientific form of communication, that is expansion of the system. This means growing of cognitive capacities, growth of collective intelligence under the ethos of science. Locally, express conditions of possibility of appearance of scientific communities and their consolidation.

The eponymy appears not as signal of prestige but as indicator of scientific growing as form of organization and specialization. Although Plebanski is a foreign last name, the possibility to stay there, to develop his work within that place, and to reach a symbolic status in a semantic community that is organized in a network of meaning around his work, express self-organization dynamics of science. Then eponym not only gives a function to indicate prestige, shows a geographical penetration of scientific institutions and global dynamics of scientific systems.

The work of Collazo et al shows an important step to induce analysis on other areas of sociology of science and social epistemology. Introduce the rhetoric figures as a cybernetic instrument that make able to observe systemic possibilities of scientific community formation. Eponymy as a Scientometric tool sounds good as a promising methodology.

Contact details: gjaime.velez@udea.edu.co

References

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Author Information: Susann Wagenknecht, Aarhus University, su.wagen@ivs.au.dk

Wagenknecht, Susann. “Four Asymmetries Between Moral and Epistemic Trustworthiness.” Social Epistemology Review and Reply Collective 3, no. 6 (2014): 82-86.

The PDF of the article gives specific page numbers. Shortlink: http://wp.me/p1Bfg0-1uJ

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‪Questions of how the epistemic and the moral, typically conceived of as non-epistemic, are intertwined in the creation and corroboration of scientific knowledge have spurred long-standing debates (see, e.g., the debate on epistemic and non-epistemic values of theory appraisal in Rudner 1953, Longino 1990 and Douglas 2000). To unravel the intricacies of epistemic and moral aspects of science, it seems, is a paradigmatic riddle in the Philosophy and Social Epistemology of Science. So, when philosophers discuss the character of trust and trustworthiness as a personal attribute in scientific practice, the moral-epistemic intricacies of trust are again fascinating the philosophical mind.  Continue Reading…

Author Information: Kristina Rolin, University of Helsinki, kristina.rolin@helsinki.fi

Rolin, Kristina. “‘Facing the Incompleteness of Epistemic Trust’ — A Critical Reply.” Social Epistemology Review and Reply Collective 3, no. 5 (2014): 74-78.

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Recent years have witnessed an emergence of a novel specialty in social epistemology: the social epistemology of research groups. Within this specialty there are two approaches to understanding the epistemic structure of scientific collaboration. Some philosophers suggest that scientific knowledge emerging in collaborations includes collective beliefs or acceptances (Andersen 2010; Bouvier 2004; Cheon 2013; Gilbert 2000; Rolin 2010; Staley 2007; Wray 2006, 2007). Some others suggest that the epistemic structure of scientific collaboration is based on relations of trust among scientists (Andersen and Wagenknecht 2013; Fagan 2011, 2012; Frost-Arnold 2013; Hardwig 1991; Kusch 2002; de Ridder 2013; Thagard 2010; Wagenknecht 2013). In the former case, a research team is thought to arrive at a group view which is not fully reducible to individual views. In the latter case, each team member is thought to rely on testimonial knowledge which is based on her trusting other team members. These two models are not exclusive and competing accounts of the epistemic structure of scientific collaboration. They can be seen as two parallel models for understanding the special nature of scientific knowledge produced in collaborations. Sometimes scientific knowledge in collaborations takes the form of collective acceptance, sometimes it is an outcome of trust-based acceptance, and at other times it takes some other form.  Continue Reading…