Chomsky and Foucault on human nature and power

Notes from a televised debate between N. Chomsky and M. Foucault in 1971 (video and transcript).

Chomsky begins with examples from linguistics to illustrate the notion of "innate structures". Children are successful in learning the language because they can use "innate language" or "instinctive knowledge" to transform limited data they get exposed to into organized knowledge. This instinctive knowledge, which allows children to build complex knowledge structures from partial data, is a fundamental constituent of human nature. Such a constituent (a collection of innate organizing principles) must be available in other domains, such as human cognition, behavior, and interaction. This is what Chomsky refers to as human nature.

Foucault mistrusts the notion of human nature - it is one of the concepts that while not being strictly scientific, has the ability to "designate, delimit and situate" certain types of discourses. For Chomsky it is ok to start with the concept of human nature as somewhat mystical (similar to gravitational forces or other scientific concepts) and later explain it through physical components (e.g., neural networks). Chomsky describes his approach as looking at the earlier stages of scientific thinking (great thinkers, more specifically) and understanding how they were able to arrive at concepts and ideas not available to anybody before.

Foucault makes a distinction between individual attribution of a discovery and collective production of knowledge, which can be referred to as "tradition", "mentality", or "modes". The former has been highly valued, while the latter is usually negativized. Another distinction is between knowledge as human activity and truth. The latter may be hidden from humans, but it will be unveiled. Attribution and relation to truth are interconnected. Throughout history we see examples of how the subject of truth (the individual revealing it) has to overcome myths and common thought, he has to "discover". What if this close relation of subject to truth is an effect of knowledge? What if truth is a complex non-individual formation? Can we replace individuals in the production of knowledge?

This position highlights a difference between Chomsky's and Foucault's approach to creativity. According to Foucault, Chomsky had to introduce the speaking subject into linguistics because language has been commonly studied as a system with a collective value. In language we have a few rules and elements and an unknown system of totalities that can be brought to light by individuals. In the history of knowledge, it's similar, but one has to overcome the dominance of individual creativity to show that there are rules and elements that can be transformed without explicitly passing through an individual.

Throughout the debate both scholars touch on many concepts from science and politics. Some of them are described below to highlight their differences:

Concept	Chomsky	Foucault
Domain (Focus)	Language	Knowledge
Human nature	Comprised of innate structures that allow for learning and arriving at complex knowledge based on partial information	A historical construct that can organize knowledge, but also can delimit how we see human behavior
Creativity	A common human act of thinking about a new situation, describing it and acting in it	An individualistic act that has been emphasized throughout history without looking at general communal rules that are behind it
Freedom	Limited number of rules with infinite possibilities of application	"Grille" of many determinisms that affects how we arrive at knowledge and understanding
Ideal model of society	A federated, decentralised system of free associations, incorporating economic as well as other social institutions	No such model can be proposed, it is more important to expose the power that controls society, especially institutions such as education and medicine that appear neutral

Somewhere in the middle, Chomsky also tried to bring their differences closer:

CHOMSKY: ... That is, I think that an act of scientific creation depends on two facts: one, some intrinsic property of the mind, another, some set of social and intellectual conditions that exist. And it is not a question, as I see it, of which of these we should study; rather we will understand scientific discovery, and similarly any other kind of discovery, when we know what these factors are and can therefore explain how they interact in a particular fashion.

While Foucault didn't completely agree to that, the conversation was still building upon each other's ideas:

FOUCAULT: ... ultimately we understand each other very well on these theoretical problems. On the other hand, when we discussed the problem of human nature and political problems, then differences arose between us. And contrary to what you think, you can’t prevent me from believing that these notions of human nature, of justice, of the realisation of the essence of human beings, are all notions and concepts which have been formed within our civilisation, within our type of knowledge and our form of philosophy, and that as a result form part of our class system; and one can’t, however regrettable it may be, put forward these notions to describe or justify a fight which should - and shall in principle – overthrow the very fundaments of our society. This is an extrapolation for which I can’t find the historical justification.

Mapping scientific fields, domains and specialties

I'm embarking on a new project that focuses on mapping research fields and studying the evolution of certain concepts and research communities. I have a certain field in mind that I'd like to investigate, but first I need to learn more about scientometrics and mapping of research domains. This is a first in the series of notes from my readings - a review chapter in the Annual Review of Information Science & Technology (ARIST) titled "Mapping Research Specialties"".

The chapter defines research specialty as a self-organizing network of researchers that tends to study the same research topics, attend the same conferences, publish in the same journals, and also read and cite each others’ research papers.

Other definitions of research specialties:

• Kuhn (1970) - communities of one hundred members, sometimes less 
• Price (1986) - an “invisible college” of approximately 100 “core” scientists, monitoring the work of individuals who are rivals and peers by reading about 100 papers for every one published
• Lievrouw (1990) - a set of informal communication relations among scholars or researchers who share a specific common interest or goal 
• Small (1980) - consensual structure of concepts in a field, employed through its citation and co-citation network 
• Rogers, Dearing, and Bregman (1993) - a family tree in which earlier studies influence later studies

The term "specialties" rather than invisible college allows to avoid the assumption that the researchers are in frequent informal communication.

Fig. 6.2 from "Mapping research specialties"

Research specialties are therefore an interconnected group of researchers that has their own knowledge base with its own concepts, paradigms and validation standards, and uses particular channels of formal and informal communication.







Studies of research specialties are connected to the key questions raised by Chubin in his 1976 review of the field "The Conceptualization of Scientific Specialties":

1. What are the social and intellectual properties of a specialty? 
2. How do specialties grow, stabilize, and decline? 
3. What are the temporal and spatial dimensions of a specialty? 
4. How do specialties vary in size, scope, and life expectancy? 
5. What are the institutional arrangements that support specialties? 
6. What impact does funding have on the kind and volume of research produced in a specialty?
7. What kinds of communication relations sustain research activities in a specialty? 

The following approaches are used in the studies of research specialties:

1. The sociological approach (seems to be much more developed than others): science as an institution (Merton); science as a system of beliefs (Bloor, Barnes, Collins); science as culture (Latour, Woolgar, Knorr-Cetina); science as collaboration and competition (Whitley, Gibbons); science as boundary making and demarcation (Gieryn)
2. Bibliographic or bibliometric: relevance (topics, novelty, availability, etc.); citations and co-citations; author co-citations; co-word analysis
3. Communicative approach: knowledge diffusion through informal channels and discourses and rhetoric in science 
4. Cognitive approach: paradigm shift (Kuhn) and branching of ideas (Mulkay)

Mapping research specialties helps to find the structure and dynamics of a research specialty and can include:

1. A map of the network of researchers and research teams involved with the specialty.
2. A map of the base knowledge supporting research in the specialty.
3. A map of current research topics in the specialty.

 A map of a specialty is a representation of the structure and interconnection of known elements of the specialty, which includes research topics, teams, concepts, authorities, archival journals, research institutions, and technical vocabularies. Mapping techniques often include bibliometric methods, such as reference co-citation analysis, bibliographic coupling analysis, co-authorship analysis, author co-citation analysis, co-word analysis, paper to paper citation analysis, journal to journal citation analysis, and journal co-citation analysis.

Others goals of mapping include:

• Mapping the social network of researchers - identify and characterize researchers and teams of researchers and their sponsoring institutions in terms of productivity, impact of research results, weak ties, levels of participation and collaborations. 
• Mapping the base knowledge in the specialty - concepts, theories, methods, controversies
• Mapping the topical structure 
• Mapping the relations - researchers, concepts, and topics 
• Mapping changes - shifts in base knowledge and topics, new subtopics, productive researchers, changes in funding

Techniques of mapping can include surveys of subject matter experts, bibliometric techniques (see above), web content analysis, and analysis of formal literature (most developed and frequently done).

The conclusion is not very optimistic though:

The problem of mapping specialties is complex and poorly defined. A number of techniques have been developed and applied. Each of these techniques reveals some separate aspect of the specialty. For example, co-authorship analysis uncovers the social structure of collaboration and research teams in the specialty, co-citation analysis uncovers structure of base knowledge in the specialty, and bibliographic coupling analysis reveals research subtopics. In and of themselves, these analytic techniques are inadequate as tools to map the whole research specialty: the social structure of researchers, the base knowledge they use, and the research topics they study. ... the metaphor of the blind men and the elephant is appropriate, as each analytic technique reveals the specialty in some limited aspect.

What is the solution for examining a specialty as a whole? Combine as many existing techniques as possible or develop some new techniques?

Cyberinfrastructure studies overview

In their introduction to the special issue on sociotechnical studies of cyberinfrastructure (CI) and e-research Ribes and Lee identify current themes and methodologies of CI studies (Computer Supported Cooperative Work (CSCW), 2010, Volume 19, Issue 3, pp 231-244, doi: 10.1007/s10606-010-9120-0)

Cyberinfrastructure (CI) is one of the current terms for the technologies that support scientific activities such as collaboration, data sharing and dissemination of findings. CI features that distinguish it from other CSCW work include: community wide and cross-disciplinary scope, computational orientation, and end-to-end (data-to-knowledge-to-user) integration.

Themes in CI studies:

1. Relationality. What is supporting the work of another and who is sustaining those relationships?
2. Integration of heterogeneity. CI involves computer specialists, data and information managers, domain scientists, and so on, but also non-human actors such as sensors and databases.
3. Sustainability. What makes CI a long-term resource?
4. Standardization. Ways to achieve integration on the technical and human levels.
5. Scale. How to plan for change and growth in the number of collaborators, the quantity of data, and the geographical reach.
6. The distribution between human work and technological delegation. 

Methods include historical, ethnographic, documentary, and interview-based approaches that focus on the following:

• Investigations of ongoing planning, development and deployment efforts 
• Activities of maintenance, upgrade and breakdown
• Adoption of certain expressions of scientific activity and changes in their use
• Adoption of new technological artifacts

Units of analysis can be a project or CI as a whole (focus on national policies and funding incentives). The introduction concludes by calling for more studies:

The stories of cyberinfrastructure are revealed by looking across multiple levels of granularity, various facets of social life, and diverse technological actors. Much remains to be studied in the areas of supporting domain specific practice, data sharing and curating, and infrastructural organizings. This is an exciting time for CI studies. Research is occurring in new and unexpected places, drawing on and bringing together the traditions of CSCW, information science, organizational studies, and science and technology studies. This cross-pollination, as exemplified by the papers in this issue, seems to be not only fruitful, but also very necessary.

Climategate study - simpler methods into the mix

Image from a HuffPo article by Shan Wells

Climategate was a controversy unfolded in November 2009 after thousands of emails and files from the Climatic Research Unit (CRU) at the University of East Anglia (UEA) were published online without the owners' consent. The climate change opponents used the content of the emails to argue that scientists manipulated data to prove their argument for human responsibility of climate change. Several investigations didn't find any scientific misconduct at the CRU, but the reports called for opening up access to research data and more transparency in methods and communication of results (see Climactic Research Unit email controversy in Wikipedia).

Controversies are always hard to sort through, but they present an interesting research case for those like me who are interested in discourse, language, and media. A recent study "The creation of the climategate hype in blogs and newspapers: mixed methods approach" (paywalled) looked at the Climategate controversy and compared discussions in blogs and newspapers.

Newspaper and blog data were collected from the LexisNexis Academic database using the search term ‘climategate’. Two methods were used to analyze the data: a) ARIMA (Auto Regressive Integrated Moving Average) modeling to create a model of the daily frequencies of postings and to examine the mutual influence of newspapers and blogs and b) semantic co-word maps of blogs and newspaper headlines to compare framings of climategate.

The results of the modeling seemed a bit confusing as they showed a significant link between a high number of blogs and a high change in newspapers articles (either increase or decrease) on the same day. (I'd really like to see simple descriptive statistics of posts per day, etc. Also, a pre-print where all the images and tables are at the end of the article is very hard to read). At the same time an increase in newspaper articles on one day had no effect on the number of blog postings on the next day. The conclusion of the article is that blogs influenced newspapers, but not the other way around. The semantic maps showed (predictably) that the blogs used a more informal language and framed the topics more negatively, while the newspapers were more formal and stayed more neutral. Both blogs and newspapers picked up similar sub-topics, such as climate change, scientists, and so on, although the word "climategate" occurred more in blogs.

Several thoughts / questions upon reading this interesting, although a bit too methodologically complicated for such simple variables and questions, study:

• How different are "traditional" and "new" media nowadays? They may be still different in their language style, but what about the speed of publication, audiences, contributors, and so on? The headlines don't get to the differences in main posts and comments either.
• The word "climagate" did originate in a blog, but it was a journalist who picked it up and popularized it via a newspaper-hosted blog (see Climategate: how the 'greatest scientific scandal of our generation' got its name). Does it change the conclusion that "blogs were independent of the attention in newspapers" (p. 20) if journalists write for both media?
• It would've been helpful to establish the actual sequence of events via an additional documentary analysis. The paper argues that the word "climategate" originated in blogs, which promoted the hype. But according to the Wikipedia article, news about emails release were published almost simultaneously in blogs and newspapers - on November 20, 2009. So is the hype about the word or other, more nuanced exchanges and actions as well?
• Three blogs received links to leaked documents. It seems that it was intentional - the blogs were skeptical of climate change. Did it matter for how the hype have originated and developed? Again, what is the connection between the actual controversy and its naming as climategate?
• How can the link between the large number of blog posts and the decrease in newspapers articles be explained? More quotes and examples of interactions and influences between blogs and newspapers could be very helpful in illustrating all the findings.

Overall, it seems that the studies of controversies benefit from careful tracings of words and actor connections rather than from complicated modeling that is rather confusing and not so eye-opening.

Summer school on synthetic biology

During the week of September 15-19, 2014 I participated in the summer school on societal implications of synthetic biology. Organized by Kristin Hagen and Margret Engelhard from the European Academy of Technology and Innovation Assessment and by Georg Toepfer from the Center for Literary and Cultural Research Berlin, it was held in Berlin, Germany, at the Center for Literary and Cultural Research.

Participants came from different countries - Austria, Italy, Germany, the Netherlands, Canada and the United States. Similarly, their backgrounds were quite diverse - biology, chemistry, philosophy, sociology, political science, and communications. The main goal of the school was to have an interdisciplinary discussion about synthetic biology as an emerging area of science and its implications for society. Participants wrote papers and presented them at the school. Additionally, several experts from various fields gave their talks. Below is a short summary of what we talked about:

The meanings and metaphors of life. Synthetic biology inevitably raises questions related to our understandings of life. On one hand, there is no universal definition of life and both philosophers and scientists continue to ponder over whether it is even possible to come up with such a definition. On the other hand, there may be no need for such definition, because a) we have an intuitive understanding of what life is and adapt as it changes, and b) having limited definitions works for specific purposes, such as understanding of how to create an artificial cell or argue against the scientific possibility of creating life from scratch. Metaphors that we use to answer the grand questions of life or to promote scientific advancements in synthetic biology bring together the domains of nature, artificiality, control, and aesthetics. Those metaphors are not “innocent” as they open some opportunities and close others.

Synthetic biology (SB) as a field. Synthetic biology is not a homogeneous discipline, it is a fuse of approaches that draw on synthetic chemistry, genetic engineering, and bioinformatics. The engineering of metabolic pathways, which allows to use bacteria and other microorganisms to produce chemicals, plays an important role in SB breakthroughs. Chemical synthesis of DNA, which allows a synthesized DNA to be inserted into an existing organism, is another important area of synthetic biology. The presentations that explained various types and flavors of synthetic biology talked about cells, pathways, chassis, microbes, reproduction, and evolution; they were colorful and full of exciting possibilities. We talked about promises of synthetic biology a lot, but I don’t think that science necessarily needs promises to justify its existence. As someone pointed out, science is a quest for knowledge, it should be interesting and exciting as such. I’m not sure science is a pure quest for knowledge, considering the convergences between science, technology, and industry. Nevertheless, I completely agree that it is exciting to learn about the world even if it's not clear whether this knowledge has applications.

Forms of communication and public dialog. Previous debates, such as the mad cow disease or GMO debate, and the resulting negative reactions demonstrate the importance of transparency in public communication of science. Early public engagement is seen as a way to improve understanding and acceptance of technology. On the other hand, the goal is not simply to promote public understanding and acceptance of technoscience, but rather to let voices of the public contribute to decision-making and regulatory frameworks. Many forms of public engagement, including polls, surveys, citizen panels, public discussions, and so on, have been promoted in the EU, and the results seem to indicate that even though not many people have heard about synthetic biology, many see continuities with previous scientific advancements and technologies and are willing to consider both positive and negative aspects of it.

Even from the short overview above it is obvious that there is a great diversity in the issues surrounding synthetic biology and approaches to their evaluation. Can they be integrated or synthesized? My own suggestion is to take a problem- rather than a debate-oriented approach and look for solutions to specific problems, while avoiding taking things for granted. Everyone has their interests and values and even the best intentions may result in bad outcomes. To use M. Foucault’s approach, we need to examine the order of things and the complex arrangements of what’s visible and hidden and what or who is included and excluded.

It was a week of stimulating discussions. The atmosphere was very friendly and collegial, and the disagreements were often phrased as humorous, slightly sarcastic remarks over dinner or drinks. My take-away from this summer school is that interdisciplinary dialog is possible, necessary, and fruitful. It works provided that we have ample time to interact and go beyond formalities (i.e., beyond formal presentations and opinion polls). The school has ended, but the work continues. We will revise our papers based on collective feedback, and they will become chapters in a forthcoming book.

See also:

• a school press-release with pictures
• a great website by Britt Wray, one of the school's participants, who writes about synthetic biology, among other things, in a much more exciting way

Summary of drivers and barriers in data sharing

Nice summary of the drivers, barriers, and enablers that determine stakeholder engagement based on expert interviews in Dallmeier-Tiessen et al., 2014, Enabling Sharing and Reuse of Scientific Data (restricted access).

Drivers and benefits

• Societal benefits - economic/commercial benefits; continued education; inspiring the young; allowing the exploitation of the cognitive surplus in society; better quality decision making in government and commerce; citizens being able to hold governments to accountable.
• Academic benefits - the integrity of science; increased public understanding of science.
• Research benefits - validation of scientific results by other scientists; recognition of their contribution; reuse of data in meta-studies to find hidden effects/trends; testing new theories against past data; doing new science not considered when data was collected without repeating the experiment; easing discovery of data by searching/mining across large datasets with benefits of scale; easing discovery and understanding of data across disciplines to promote interdisciplinary studies; combining with other data (new or archived) in the light of new ideas.
• Organizational benefits - publication of high quality data and citation of data enhance organizational profile; preserved data linked to published articles adds value to the product; data preservation is more business; reputation of institution as “data holder with expert support” is increased; combining data from multiple sources helps to make policy decisions; reuse of data instead of new data collection reduces time and cost to new research results; use of data for teaching purposes.
• Individual contributor benefits - preserving data for the contributor to access later — sharing with your future self; peer visibility and increased respect achieved through publications and citation; increased research funding; when more established in their careers through increased control of organizational resources; the socio-economic impact of their research (e.g., spin-out companies, patent licenses, inspiring legislation); status, promotion and pay increase with career advancement; status conferring awards and honors.

Barriers and Enablers are Related to:

• Individual contributor incentives
• Availability of a sustainable preservation infrastructure
• Trustworthiness of the data, data usability, pre-archive activities
• Data discovery
• Academic defensiveness
• Finance
• Subject anonymity and personal data confidentiality
• Legislation/regulation

Philosophy and science: A need for Ph in PhD

An interesting piece published recently in the Science magazine: Shaking Up Science by Jennifer Couzin-Frankel. It's behind the paywall and pretty long, so here is a quick summary.

The essay is a story about two biology scientists, Ferric Fang from University of Washington and Arturo Casadevall from Albert Einstein College of Medicine in the Bronx, New York. They were brought together by "disenchantment", i.e., they both had worries about what is going on in academia and science:

Discovery for its own sake was being sidelined by a push to publish in high-impact journals. Funding was scarcer than ever. Scientists focused on narrow fields and often couldn't communicate their professional passions at a cocktail party.

They were both editors of an immunology journal, so they started writing opinion pieces about grants, peer review process and so on. At some point they got interested in research misconduct, more specifically, how many papers are being retracted, where and why. First, they wanted to see if there is a connection between a journal's impact factor and its retraction rate. They searched the PubMed database and found a robust correlation - the higher the impact factor, the more retractions the journal had.

Then they looked closely to retractions between 1977 and 2000 and found that about 67% of all the retractions were attributed to scientific misconduct, including fraud and plagiarism.

The next step was (and it's usually the most difficult one) to figure out why it happens. I like the possible explanation, but it's not clear from the essay whether it was supported with evidence or not. It makes a lot of sense though.

The scientists believe that the race for grants and funding encourages misconduct.

"It's all about money," Fang says. "How can you be sure that you get money?" The answer comes back to publications—and sometimes skirting the rules to get them.

The story up to this point is more or less obvious. A lot of people talk about problems with depending on grants in funding science and scientists (soft money) and peer review. What is interesting is what kind of solutions are proposed. The scientists argue for more generalized science education instead of the extreme specialization. And for more philosophical training, particularly in epistemology and metaphysics that encourages asking questions like "What is it that you know?" and "How do you know what you know?"

Even though we may never go back to making philosophy a required subject (which I had as part of my graduate studies in Russia), I think it'd be great. Asking broad questions about the nature of knowledge and, more importantly, its justifications and limits, encourages people to step back, look at the larger picture and think critically about what they're doing. By doing that the sciences can be what they're supposed to be - a self-correcting institution based on the Mertonian norms of communalism, universalism, disinterestedness, originality and skepticism.

Gender bias in science: It's real

A simple study on gender bias discussed here:

Whenever the subject of women in science comes up, there are people fiercely committed to the idea that sexism does not exist. They will point to everything and anything else to explain differences while becoming angry and condescending if you even suggest that discrimination could be a factor. But these people are wrong. This data shows they are wrong. And if you encounter them, you can now use this study to inform them they’re wrong. You can say that a study found that absolutely all other factors held equal, females are discriminated against in science. Sexism exists. It’s real.

The results are not that surprising, e.g., that both females and males are biased against females in science or that most of this bias is unconscious, i.e., scientists used rational reasons to explain why they wouldn't hire a woman.

I like the author's suggestion though: there are definitely people out there who find this situation disturbing, so it's important to disseminate this information and hopefully something changes.

Metadata friction

Metadata friction - tensions and costs in time, money, energy and attention caused by attempts to produce metadata ("Science friction: Data, metadata, and collaboration", Social Studies of Science, 2011, vol. 41, no. 5, pp. 667-690 ). Rather than viewing metadata as products (i.e., a set of descriptors or tags), the authors suggest to look at it as a process. The process of metadata production is often manual and of ad hoc quality. It is fragmented, i.e., many individuals contribute, they may do different things, such as creating websites or spreadsheets, answering questionnaires, etc. The process is divergent, i.e., several versions of metadata can be created. It is iterative because of the need to reconcile versions, repair mistakes and overcome miscommunications. It is also locally oriented, i.e., the local use of data is often privileged over the desire to contribute to the general project.

The authors offer the analogy with engineering, where friction is reduced by precision – making interacting parts mesh better – and by using lubricants. Typically, metadata discussions address precision - how to join parts (datasets) more perfectly. Metadata as process focuses more on lubrication: "the practices through which people overcome friction without precise solutions or the need to modify components." (p. 684). These practices are imprecise, therefore misunderstandings are inevitable. But they are an important part of metadata creation and exchange, therefore more attention should be paid to these practices as forms of scholarly communication.

Typology of public engagement

Here is my attempt to visualize a paper rather than type up notes:
Rowe, G., & Frewer, L. J. (2005). A typology of public engagement mechanisms. Science, Technology & Human Values, 30(2), 251-290. doi: 10.1177/0162243904271724.

Organizing a session at 4S (Society for social studies of science) annual meeting

I'm trying to co-organize a session at the 4S conference (see their CFP), so here is our call for papers (abstracts, actually):

Call for papers for a session on public engagement with science at the 4S Annual Meeting, November 2-5, 2011, Cleveland, OH

“Problematizing public engagement with science”

The discourse of public engagement with science is a prominent topic in the STS literature. Dissatisfied with conventional science education and media dissemination methods, scholars and practitioners explore alternative ways of public engagement, such as the use of digital media, scientists’ participation in school classrooms, and placements of members of the public on advisory boards of policy-making and funding institutions. Where are we with public engagement now? Are we as scholars and practitioners on the right track?

In this session, we aim to bring together researchers who are interested in investigating and problematizing ways the public is engaged with science. We would like to stimulate a discussion that draws on philosophy, methodology, and practice by seeking answers to questions such as: What methods can aid us in understanding and critical analyses of existing and prospective means of public engagement? How can researchers investigate the effectiveness, inclusivity, and underlying agendas of the means of engaging the publics? Are we as researchers missing any perspectives, methods, or tools that can facilitate better understanding and successful implementations?

Ways to contribute to this discussion may include papers that provide insight and raise questions regarding any of the following:

• Concepts and social agendas – What roles should various stakeholders (e.g., scientists, government, publics) play? What ethical and moral issues are involved? How do the core concepts, such as science, communication, and publics, influence our understanding of public engagement?
• Theories and methodologies – Methods, assessments, and perspectives that hold promise for investigating issues around public engagement with science and science discourse.
• Tools and rules – Existing means for promoting public engagement in science. What are the strengths and limitations of these means? What can be learned from reports of success or failure of existing initiatives of promoting public engagement with science?

We aim to organize at least one session submission for the 4S conference on the topic of problematizing public engagement with science. If we have sufficient number of co-presenters, we will submit complementary sessions themed accordingly to the submissions received.

Deadline for submitting abstracts: March 10, 2011

Please submit abstracts (250 words) to: Inna Kouper at inkouper at indiana dot edu

Session co-organizers: Lai Ma, Inna Kouper, & Thomas Fennewald (Indiana University)

Research: The construction of anecdotal evidence

A recent article in the Science, Technology and Human Values titled Experts and Anecdotes
The Role of ‘‘Anecdotal Evidence’’ in Public Scientific Controversies looks at how the notion of anecdotal evidence is constructed in public controversies. The analysis is informed by the concepts of boundary work, lay knoweldge, and expertise.

The main argument is that scientists perform boundary work, i.e., they define the boundaries of what can be considered science and legitimate knowledge. Other actors, such as activists or affected people, challenge the boundaries by re-framing the risk in terms of their particular social conditions. The authors try to show how nonexpert claims in the cases of mobile phones and MMR controversies were ignored, welcomed, or altered during the interactions of experts and officials. To put it simply, nonexperts said that anecdotal evidence is important, but experts dismissed it until publicly expressed dissatisfaction reached a certain point.

The argument of the paper is not convincing. It seems that the authors used the concepts of boundary work and lay knowledge as assumptions that defined their conclusions. In other words, the roles of experts and nonexperts have initially been assumed to be the way they were described afterwards. In such case it's really difficult to find anything less obvious that the expert - layperson divide about the meaning of science and evidence. The questions that should be asked here are "Why do we need to challenge the existing conceptualizations of evidence?", "Why anecdotal evidence should be considered a valid kind of evidence?", and "Can the public legitimize their concerns by making experts accept anecdotal evidence or should they construct their legitimacy by other means?"

Showing that something is constructed (e.g., the boundaries of science) is not enough. It is also necessary to show why showing the constructive character is necessary and why the construction needs to be challenged. I also think that large agents such as the mobile phone industry and the producers of vaccines should have been included in the analysis. The tension is not only between experts and non-experts, it is also among different interests of a variety of agents.