Fiske & the Mutual Admiration Society

I have often wished we could simplify our social status signalling to the dog greeting technique.  Start with your prior belief about your station in the hierarchy, and wag your tail / stand still / or shrink your posture accordingly; exchange  a butt sniff;  growl snap a bit if its not clear who is where; and if necessary, the superordinate rests its jaw on the subordinate’s neck.  Everybody updates, and all proceed, interacting in accord with their station.  Is status unequally distributed?  You bet.  But guess what: mutually destructive fights are off the table.  Group function (a society with a chance to reproduce itself through time).


In her recent effort to re-establish the pre-science blogging / social media norms of academic communication (and perform her role of protecting her intellectual offspring), Professor Susan Fiske, a multiple award winning occupant of an endowed chair at Princeton, and the founder and leader of a very successful pyschology lab, invokes the “terrorist” bogeyman, and thus got the attention she was seeking. I discuss her use of that term below.  First, I want to discuss the Mutual Admiration Society she invokes.


The Function of a Mutual Admiration Society

Fiske decries the rise of “Methodological terroroists,” a group of self-appointed data police who operate outside of professional norms by publishing critiques of published work on non-refereed blogs (shared via social media), often impuging the motives of the authors, and far worse. Yep, there be trolls, unbridled sexism, and much nastiness online, including academia. And since none of us have read all of the social media posts or blog posts that she has in mind when leveling this charge, I am content to accept her judgment that a non-zero, and perhaps 20%, 40% or even 70% portion of the relevant corpus would be judged by many as having met her standard.  But I want to focus on her concern about the removal of peer review from publishing critiques that the scholarly community (and beyond) can access.

Here is Fiske, with most of her metaphoric hyperbole[1] stripped.  These bloggers

are ignoring ethical rules of conduct because they circumvent constructive peer review… Ultimately, science is a community, and we are in it together. We agree to abide by scientific standards, ethical norms, and mutual respect.

This bit is worthy of your consideration.  Let’s break it down.

First, “science is a community.”  Sure, most who self identify as scientists are likely modernists who believe (implicitly) that science is primarily a method, not a set of norms and institutions communally constructed.  But I suspect all but the most hardline of such folks agree with Fiske on this claim.

Second, “We agree to abide by scientific standards…”  Sure, its hand wavy, but her post APS editor approved communication is brief, and it’s a big tent, so I am willing to believe that a super majority of us are on board, conditional on the content of the hand wave.

Third, “ethical norms…”  Ahh, this is getting interesting.  Let’s focus on bloggers who post about research.  That is, engage in professional commentary that is publicly available but was not subject to any form of review (peer or otherwise).  I think a majority of us (who read blogs) can agree that sarcasm, snark and wit are celebrated in much of academic blogging.  And when Keiran Healy recently got “Fuck Nuance” accepted in a peer review discipline journal (with the three word abstract: “Seriously, fuck it.”), I cannot be the only one who thought “blog culture just crossed over.”  Yup, the 1960s / 1970s tear down of formality in (especially American) culture that was then led in the 1980s/1990s by Silicon Valley corporate culture, had hit academia and the tail end of the baby boomers (who engage the culture relatively little) don’t like it.  Enjoy the irony.

But just because we can raise late boomers who advocate Fiske’s position on their own petard does not weaken the position itself.  Fiske’s implicit notion that ethnical norms are static is, of course, foolish.  She would presumably acknowledge that.  But we would be equally ridiculous to pretend that the changing winds Andrew Gelman referred to are not sudden and rapid (as he observes), but more to the point that the challenge to the ethnical norms which the collapse of the cost for sharing one’s ideas in the commons had produced have been jarringly quick.  I suspect that many of us who blog dramatically underappreciate our (potential?) “influence.”

And if you don’t think of us as jointly producing news norms, including the subset of ethical conduct, well, may I recommend that you do some reading on norm construction.  This, then, is why Fiske’s invocation of ethical conduct stands out to me.  Should we do so in an unregulated market of conduct?  Or should we address the collective action problem, pay the coordination costs, and explicitly negotiate ethical codes of blogging conduct?

Let me first observe the obvious problem that with few of the tens of thousands of the stake holders in academic societies are engaged in the blogosphere.  Quelle surprise!   [insert banal observations on youth, technology, and rebellion]  So, yeah, the societies that have solved the collective action problem are not a promising vehicle for addressing this issue.

Second, well, the funny thing about solving collective problems is that it is really hard.

And it turns out that transaction costs are, well, costly.

So… Huh.

I guess the unregulated to market of conduct would be the default.

And here we are.

Finally, Fiske writes, “mutual respect.”  Ahhh.  Here, I suspect, we would find a major divide.  Those who contribute to, engage, and value the academic blogosphere (Open Access FTW) would, I strongly suspect, have a broad spread of views with respect to the value of, much less empirical content of, “mutual respect” than those who do not engage / value the blogosphere.  Most of the latter, I suspect, would endorse the “mutual respect” norm, comparatively few questioning its value.

And this returns us to the butt sniffing dogs.  Mutual respect is the primary lubricant that defines the Mutual Admiration Society.  I like to describe it this way.  Two academic peers encounter one another at a professional meeting, and perform the following ritual.

Academic A: “You’re so smart.”

Academic B: “You’re so smart!”

Butts sniffed Mutual recognition confirmed via mutual respect, they are free to part company, and even talk shite about one another’s work, privately. I think dogs’ solution to this collective action problem is much more transparent.  As a general rule, I detest ritual.  But I’d sign up for this.

Like all social institutions, the Mutual Admiration Society is both functional (permits society to operate efficiently), and produces perverse (aka, collectively suboptimal and, individually, unequally distributed) outcomes.

Welcome to social science.  You did not need me to point that out to you.  Well, because you are (likely) neurotypical and spend little time as an outsider to your social and professional circles, observing it and trying to figure out “Why the fuck do they do that?” perhaps you needed the assistance of an aspie who does spend most of his life standing outside his personal and professional circles wondering about precisely that question.

And Fiske’s post produces a moment to think about these four issues.  What do you make of the Mutual Admiration Society, by which I mean:

  • What do you think are its benefits?
  • What do you think are its perverse outcomes?
  • How might we improve it (by which I mean re-design norms and institutions such that we produce a self-enforcing new equilibrium).

And what do you think about professional ethical conduct in the blogosphere?  Quixotically wishing it away is not a conversation that interests me, but I want to encourage you to think about whether the CA problem should be addressed, and if so, how?


On “Terrorists” and “Terrorism”

Having spent a non-trivial portion of my professional life thinking, teaching and even writing about “terror,” I would be remiss not to chastise Fiske for her horse shite coinage of the term “methodological terrorist.”  If she hadn’t published several valuable, highly visible, articles on the process that dehumanization plays in making torture acceptable, I’d roll my eyes and declare her asinine choice unworthy of my disk space.  But for someone so prominently familiar with the impact of that very term upon our species’ willingness to engage barbaric behavior , who then decries the shaming behavior of those she has just de-humanized via pejorative labeling…

What’s the word I’m looking for?

Unconscionable.  Yup, that’s the word.

Such behavior is unconscionable.  Shame on you, Professor Fiske.  You need to hold yourself above such conduct.  It is entirely possible for you to make your point constructively without engaging in such shockingly poor judgment.

But, damn.  Props for those clicks!


NB: The original version of this post did not include the dog cartoon.

[1] By metaphoric hyperbole I am specifically referring to her use of the terms “destructo-critics,” “targets,” “methodological terrorist,” and “mob rule.”


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Learning to Drink from a Fire Hose

This is the second in a series of guest posts by Nate Monroe.

In “Week 1: How to Succeed in Grad School,” Will does an excellent job covering the arc of our Week 1 readings (they are identical across our syllabi). His description of what he tried to impart to his students is pretty much in step with what I did, so I won’t rehash that here. Instead, I’m going to talk a little about some minor deviations, mostly by way of framing and extra emphasis, that I pursued in my run through Week 1.


Before I get to that, I’ll make a comment on tone. Over the past four years, I’ve developed a reputation among our grad students as being a bit… scary. This is mostly unintentional. I say “mostly” because I do intentionally have very high expectations—especially when it comes to students being prepared for seminar—and I have enforced those expectations pretty harshly. I do this especially in the intro class because (1) the students have a lot to learn in a hurry, and few can do it without working exceptionally hard, and (2) I know my colleagues expect them to be prepared in future seminars, so I see it as my job to set that expectation right off the bat.

I don’t mind my scary reputation, per se. But I’ve begun to worry that it inhibits student learning in seminar, as they’ve seemed increasingly nervous and tongue-tied in recent semesters. I’m making a concerted effort this semester to foster a less nerve-racking environment: fewer cold calls, fewer moments of icy silence, (slightly) fewer reprimands for students that don’t fully prepare. We’ll see how this goes, and if the tradeoff (i.e., I assume students will be a little less likely to read fully and carefully as a result of the “kinder, gentler” me) is worth it in the end.

I tried to give it to them pretty straight in Week 1: you’re no longer an undergrad; this will be really, really hard at times; you need to treat it like a job; this first semester—and maybe the whole first year—will feel a little like drinking from a fire hose; our expectations are very high; etc.

I then pointed out that the job market is just around the corner. Since first year students have a hard time conceptualizing four (or more) years as just around the corner, I asked them to count backwards from the day they hoped to be on the market. This is the relevant end point because students’ ultimate goal (at least in the first round of their careers) should be to secure a tenure track position.

I asked my students to assume that they want to be on the market in the fall of their fifth year (a slightly optimistic but certainly not unattainable goal), and to imagine what their file needs to look like to have a shot at interviews: excellent letters from three faculty members and several publications, including at least one on their own or with a grad student colleague. We then worked back through the math of how long it takes to write, revise and publish papers, and considered how many other things (e.g., RAships, coursework, technical training) needed to happen for them to be ready to start that process. We also talked about the need to start making a good impression on faculty now (more on that a little later).

Certainly this is a bit of an unnerving thought exercise (come to think of it, maybe this didn’t help my attempt to be less scary…), but my intention was to jar them out of any remaining sense that grad school is “just the next step in their education.” It seems absolutely essential to me that students come right out of the gate understanding that this is professional training; an apprenticeship, where there is no time to waste.

To this end, I also emphasized the immediacy and importance of cultivating their professional reputations. I pointed out that their reputations had already begun to take shape, at orientations and first class sessions and would quickly be shaped by their participation at department talks and meetings in faculty offices and circulations of paper drafts and presentations at conferences…on and on; and that this would continue, indefinitely, for the rest of their careers.

I stressed this point because, in my experience, first year graduate students fail to appreciate how small the world is that they are entering, and how long everyone’s memories are. I reassured them that there will inevitably be missteps and shared a couple of my own—I made a giant ass of myself in front of Ray Wolfinger as an undergrad, and Mat McCubbins summary comment on my final paper for my intro grad class was “and you were the one I had hope for, too…”—as evidence that a few mistakes aren’t likely to be career killing. But I encouraged them to be mindful of the impression that they are making with all of their professional activity.

Faculty aren’t the only audience, either. I reminded them that the grad students they meet today will one day be faculty and co-authors and journal editors and search committee members and lots of other useful things; making a positive impression now and building positive relationships early will pay dividends for a long time. To reinforce this point, I told the stories of how I came to know some of my regular collaborators, like Jeff Jenkins (I unexpectedly got swept up in a group lunch at MPSA), Chris Den Hartog (we bonded over a giant burrito), and Jason Roberts (we had lots of phone calls before 6 am about one of his working papers), when I was still a very green grad student.

Undoubtedly, coverage of these “professionalization” topics in Week 1 only scratched the surface. First year students will need to hear all of the lessons 10 or 100 more times, communicated in a variety of ways before they really take hold. And they’ll need to learn a lot of other lessons that go beyond the scope of a three-hour session (I appreciate that my department has developed an excellent monthly workshop for just this purpose). But I think our discussion got them off to a pretty good start.

And after class I was asked grudgingly by an older student why I didn’t scare the first years like I used to. So… progress.



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Week 4: Why Theorize?

The fourth week of my Scientific Inquiry–Theory & Inference course addresses the question “Why should we theorize,” or “What is the purpose of theory.”  It is the first of a four week section on “Theory.”  The Gordian Knot problem rears its head as I put off discussion of causal relations/causation until the fourth week of the “Theory” section.  But weeks two and three of the seminar establish a post-modern, constructivist foundation for the production of human knowledge, a small portion of which concerns regular patterns we collectively discern in the world: stylized facts.[1]


The seminar is focused on joining the community of researchers who want to explain those stylized facts: contribute to the production of that small portion of human knowledge that has blown up over the past several centuries.   This week’s readings build on the reading from week 3 on the failure of induction as a process for constructing explanations for those stylized facts, and the problems with verificationist and falsificationist accounts for explaining the success of scientific knowledge communities.


Fudging a Definition of Theory

I am interested to learn how Nate addresses this,[2] but I began the seminar telling them that I am not going to define theory–that’s their job, not only over the course of the semester, but over the course of their PhD studies, and beyond.  Indeed, I tell them, for the seminar I will treat “theory,” “model,” “framework,” and similar terms as synonyms for “explanations of stylized facts.”

I subtitled the readings “Creating Models to Explain Stylized Facts.”  They first read four chapters from Clarke & Primo’s  A Model Discipline “What is a Model?” “Theoretical Models,” “Empirical Models,” and “Explanation” (pp. 52-167).  I assume the reader is familiar with the content of these chapters, and trust it is fairly self evident that the arguments they make mesh with the post-modern, constructivist foundation provided in this course.[3]

I don’t know what Nate plans, but as noted above, I leave it to the students to decide whether they want to embrace (for the moment) Clarke & Primo’s distinction between models and theories.  During seminar I pressed the value of the models (theories, frameworks, etc.) “as maps” metaphor.  For what purpose has the researcher developed their theory?  To assess the value of the theory one should not consult a checklist of steps or any other set of criteria.  One should ask whether she finds the theory useful for its intended purpose.  Full. Stop.

This reading sets us up to debunk a lot of the garbage one routinely hears social scientists make about the standards we use to judge research reports (especially for the “top” journals and presses).  “Only work that checks the following N criteria should be in the top outlets,” is a general expression of this liturgy.[4]  to select an arbitrary moment in time for illustration, during the 1950s the liturgy for the top outlets had a particular composition.   Over the decades it has changed, marginally during most “one to five year” aggregations of time, and during some of those chunks of time rather markedly.  Though liturgy is obviously functional—science has progressed—I am arguing that it is suboptimal.  We will operate more efficiently as a community (make more regular progress developing theories we find useful) if we abandon liturgy that draws on modern, verificationist, and falsificationist understandings of scientific method as practiced by individual research teams in favor of the broader understanding of theory as maps articulated in Clarke & Primo.

Sutton & Staw (1995) [PDF here] offer a negative, connotative definition of theory drawing upon their experience as authors, peer review referees, and journal editors.  Healy (2016) exposes our inclination as audience members, discussants, and referees to demand nuance from others’ work.  Unlike the Clarke & Primo presentation, both of these works shift the reader’s point of view from knowledge producer to that of the gatekeeper.

In week one of the course I described the PhD process as a transformation from knowledge consumer to knowledge producer.  And this course makes the case for rethinking science as a practice of individuals (“method”) to the (re)production of norms and institutions that solve the collective action problems produced by the clash between our individual incentives as community members and our collective goals as a knowledge community.  The Healy article in particular offers the students an opportunity to “try on” the role of gate keeper; to consider the clash between our individual incentive as critic to “demand nuance” (independent of any finite set of stylized facts) and the author’s goal of producing an explanation constrained by the specific set of stylized facts she has designated; to discuss how they might begin to play that role over the coming years. That is, I encouraged them to appreciate that the “what’s the purpose” standard Clarke & Primo advance exposes the asinine hubris that Healy so nicely describes.

I then told them that they would be the nuance demanding ass-hat.  I argued that the nuance problem exists because we are all nuance demanding ass-hats.  The question is not whether, but instead your relative frequency: what percentage of your comments, quips, questions and retorts over the next [arbitrary unit of time] will be “nuance demands”?  If you think it is close to zero, you are deceiving yourself (which, we know from week 2 reading, we do many times per waking hour).  I encouraged them to pay attention during upcoming talks in SPGS and count the number of nuance demands.  I encouraged them to start noticing them in their seminars.  I suggested that they might find it useful to produce a typology of “nuance demands,” the better to begin purging the habit from their own repertoire.

Unsurprisingly, my students were drawn toward reading Sutton & Staw from the lens of the author: what can I learn from this article that will help me get published?  That is the obvious, self-interested view any student will first explore.  So I pushed them beyond that view toward one where they could try on the issues that Healy’s essay more naturally leads them to consider.


I sit out while they have a student-directed discussion.[5]


In week one I asked them how many had seen “The Karate Kid.”  Each one had seen it.  I asked them what was the key line from the movie.  Most of them smiled as several of them answered “Wax on.  Wax off,” a few even making a circular motion in the air with their palm. “I know you want to learn how to do research; how to ask good questions; theorize; design a study; write a paper, and get it published,” I told them.  I continued with something like the following.

If I could teach you that this semester, I would.  But I can’t.  This seminar offers no answers, and especially does not offer a recipe.  It provides you an opportunity to practice.  It has a structured set of readings that are not important on their own, but instead prompts to get you to practice.  There is no static method to learn; no recipes to master.  Switching analogies, the readings and seminar discussion are like the equipment in a gym: you have the opportunity to workout.  Whether you do is up to you.


[1] Those interested in thinking about stylized facts might check out Dan Hirschman’s post yesterday at Scatterplot.  He discusses Charles Crabtree & Chris Fariss’s comment on his recent paper on the topic.

[2] Nate Monroe and I co-developed the syllabus.  Be sure to check out his posts about teaching the course.

[3] I am not suggesting that Clarke or Primo would endorse this syllabus.  I am merely asserting that their rejection of the dominant view of (social) science taught in our discipline is consistent with our course (or vice versa).  If that doesn’t make much sense to you, no worries.  Stay tuned.  There is more to come as the semester unfolds.  :-)

[4] I am indebted to Mike Ward for this use of liturgy, though I believe he means something a bit different from my usage.

[5] If one of your reactions to this photo is “What’s up with the 2016 dudefest at ASU?” we also had that reaction to the outcome of this year’s recruitment.  Single draws and small N’s aside, we are counting on you to help us reduce the odds of repetition by encouraging your students to apply to our program, which, for the record, includes a specialization in women and politics.

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How to be a Political Scientist in 10 Easy Steps

This is the first in a series of guest posts by Nate Monroe.

In his blog post, “Teaching Theory and Inference (aka ‘Science’) to 1st Year PhD Students,” Will aptly described the motivation for and logic behind our jointly designed PhD course (At UC Merced, the course is formally called “POLI 200: Research Design in Political Science,” though I probably prefer a broader title like “The Science of Politics” or “Scientific Inquiry into Politics” or “How to be a Political Scientist in 10 Easy Steps”). I’d have been more than happy to let Will speak for both of us as he blogs his way through the course this semester; we seem to disagree very little when it comes to our approach to doing this job—at least as far as I can tell so far (Stay tuned). But he’s invited (coaxed?) me into my first adventure as a blogger.


Word Cloud from Syllabus

I’ve taken his bait, at least on a trial basis, and I’ll be writing “addendum” to his posts each week. Mine will be lagged by at least a couple weeks; our semester started a week after Arizona State University, and Will dutifully rescheduled his labor day class for the next day. I forgot to do so because I spent the day fishing (students taking my course will catch up during our final exam week).

Unlike Will, who took a decade off from teaching the intro PhD course, I’ve taught it three of the last four years. My previous syllabi included many (but not all) of the topics that we include in our current syllabus, but the bulk of my old version focused on (was preoccupied with?) causal inference and various methods for pursuing that end.

I still think causal inference to be a worthy endeavor. But as I taught the course several times, tweaking it a little here and there, I was bothered by the nagging feeling that there was a mismatch between the emphasis in the course and my own belief about the import of different parts of the social scientific process. In particular, I’m a bit of a theory monger (my colleague, Emily Ritter, likes to parody my mongering by pointing out that virtually every comment I ever make in a workshop boils down to something akin to, “So… What I think you need here is a theory…”)

I see theory as the heart of what we do. To be clear, I do not think that all theories need be formal (though I’m often drawn to such models), nor do I think that every paper must have a theory, new or old. But I do tend to think that theory is the center of gravity for social scientific inquiry. When I encounter a really great paper that develops a brand new measure, I never think, “This paper should really have a theory.” But I do tend to judge the value of even a measurement paper by asking whether the proposed measure will improve our ability to test an implication of some well-specified, clearly-stated theory. As far as I’m concerned, all roads lead back to or forward towards theory. My PhD advisor, Mat McCubbins, probably deserves a fair bit of blame for my theory zealotry; please direct all complaints to him.

Last spring, in anticipation of teaching our intro PhD course a fourth time, the nagging feeling finally got bad enough that I decided to spend a few precious summer days redesigning my syllabus. At about that time, Will came to campus for a conference on Courtenay Conrad and Emily Ritter’s book manuscript, and he and I spent the better part of the drive back from Yosemite banging our fists in agreement about all that is right and wrong in political science. As a result, I suggested we redesign our syllabi—our syllabus—as a team.

To my great satisfaction, our redesign sessions not only affirmed my instinct to expand and prioritize the role of theory in the course, but it brought about new ways of thinking about old topics. To my mind, the flow from ontology to scientific method to theory is much clearer now; the categorization of various inference strategies is more logical; and the nuts and bolts of measurement are far more complete than I could have done on my own (for my money, there’s no one better than Will at thinking about measurement). Perhaps more than anything, I’m now thinking of (and teaching about) social science as a true community endeavor.

A final caveat: the course we designed is our best attempt to begin the training for students in political science PhD programs, given our strengths and perspectives. It’s certainly not the only way to do it, and of course there’s plenty of room to debate whether it represents an improvement over other approaches. But, inasmuch as others find it useful or helpful, I would be glad to discuss the logic and logistics that might help you adapt the course for your purposes (and of course share all of the relevant materials).

Thanks for enduring my maiden blogging voyage. I’ll be back for next week’s topic, pending Will’s approval.

Nate Monroe


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Week 3: What is Science?

Having addressed negotiating grad school and reality, perception and human knowledge, week 3 of my new course, Scientific Inquiry–Theory & Inference,[1] tackles the demarcation problem: what is (non)science?  We might begin with motivating question: What can explain the explosion in practical knowledge produced by the scientific revolution?  Instead, we take it as given that students accept that there is something called “science” that would be useful to distinguish as a way of producing human knowledge from non-science modes of knowledge production.  The Babbie and Cohen & Nagel readings from the previous week set the stage for such a demarcation.


To this point the course has made a case for distinguishing pre-modern, modern and post-modern ontological positions, the third of which implies a constructivist view of human knowledge.  It seems to me that many social scientists consider the post-modern / constructivist positions as anti-thetical to science.  That position has never made sense to me.  But students are likely to have an (implicit) understanding of science as a method practiced by individuals that is predicated on a modern ontological position, or at least the rejection of constructivist understandings of human knowledge.  The course, thus, takes it as given that these issues must be confronted head on.

As such, in seminar I ask the students whether the reading in weeks two and three have, to some extent, kicked down the foundation upon which there understanding of science rested.  Many (most?) said that it had.  And discussion in seminar revealed the discomfort that produced.

I reminded them that the goal of the course is to give them the intellectual tools they  need to begin constructing, while in graduate school, their own intellectual values that will support their decision to make public their own knowledge claims (via publications). I then explained that the course makes the argument that the key to demarcating science as a process for knowledge production is to move from focusing on individual practice of a “scientific method” (as covered in elementary education and depicted in fiction) to a focus on the norms and institutions that govern community practice.  Over the course of the semester the course “rebuilds” science from post-modern and constructivist foundations that embrace what social scientists have taught us about biases in human perception,  and the problems induced by our species inherent individual engagement in status (re)construction.

The logically coherent framework the course proposes provides the first, to our knowledge, social science account of science as a knowledge community defined by norms and institutions that govern practices which solve (as yet poorly articulated, though well intuited in isolation) collective action problems induced by (a) the frailties of individual humans to produce useful/practical knowledge and (b) the ubiquity (constancy) of status competition among primates.[2]  As the semester proceeds we will use this framework to integrate “solutions” to these problems that are generally viewed in isolation and/or as false choices.[3]  In doing so we will not only expose false choices (fallacies of the excluded middle), but also highlight the number of actual trade-offs researchers face as they plan and execute projects, and make a case for why the focus on individual projects (articles, findings, etc.) is misplaced.  We will also identify a variety of attendant problems, and put ourselves in a position to make proposals about how to create / revise our norms and institutions coherently.

But this week we read standard accounts of the demarcation of science from non-science, though rely on an undergraduate text (first written in 1976) that embraces the post-modern, constructivist account.


Reading Assignments

The undergraduate text is Chalmers’s What is this Thing Called Science?, and I have them read chaps 3-7 and 15.  They read chapters one and two last week.  Chalmers cover the falsificationist demarcation effort of Karl Popper, and the subsequent revisions thereto, that still serve as the primary philosophical foundation for most of the coursework of this stripe in political science.[4]

This strikes us as important reading because it is so well known among the faculty who will be assessing these students’ as they pursue tenure track positions, and when they apply for promotion and tenure.  One could imagine skipping it–why read historians’ or philosophers’ efforts to demarcate science from non-science, especially when they were written decades prior to social scientists production of theories of collective action problems, norms and institutions, and contemporary understandings of human perception and knowledge claims?  Were a text like Chalmers unavailable, one could make a case.  But Chalmers’ treatment is wholly consistent with the major argument of the course, and is very accessible.

In addition, we assign the “Inductive Reasoning,” “Analogical Reasoning,” and “Problems
with Induction,” discussions in HJ Gensler’s Introduction to Logic.  It is an undergraduate text.  I have a brief unpublished “Primer on Inference and Logic for Political Researchers,” and in conjunction with Gensler, the students get a very briefly sketched foundation upon which to construct the case for the value of logic for theory construction and evaluation.   These two readings suffer from the conventional “one inch deep, ten miles wide” problem, and will be of much greater use to students who have had a course in symbolic logic than those who have not.  Yet when we get into the “Building Blocks” of theory construction (see this post for a course overview) in two weeks, these primer sketches will be helpful.

Pages 9-15 of Clarke & Primo’s A Model Discipline contain these sections: “Science is not what we Think it is,” “Current Practice is not Philosophy Free,” “Models are Objects,” and “Models are not Tested with Data.”  Those sections dove tail nicely with Chalmers, further undermining the “modern-falsificationist” foundation for science that are so common in the discipline.  We assign much of the remainder of Clarke & Primo in the reading for next week, so pulling out these sections and including them here serve as a nice setup for the coming week.

Finally, in chapter 6 of Doing Political Science Zuckerman has a very accessible[5] account of why we should think of science as community practice: the norms and institutions produce individual incentives that maximize the prospects that a scientific knowledge community will, on average (especially as one increases the temporal unit of aggregation over which to make comparisons), generate practical knowledge that will improve our ability to act in the world with increasing effectiveness.  That account plays a bit fast and loose with Zuckerman’s chapter.  He does not use the terminology I am using, but it is entirely consistent with my account, and hence makes for useful reading.


[1] Co-developed with Nate Monroe.

[2] While I am not a fan of Dacher Keltner‘s use of the term “power” (what political scientist could be?) his work on status competition is accessible and current.  Check out this Podcast overview.

[3] In the weeks to come I will describe why I believe status competition incentives provide a useful explanation for why these “debates” exist and tend to be so draconian (hint: think “chest beating” as an alpha male dominance behavior).

[4] I chose not to include the chapters on Thomas Kuhn’s “paradigms, normal science, crises, and revolutions,” Lakatos’s revisions to Popper, nor Mayo’s new experimentalism. Whether this is a good, or poor decision, I am uncertain.  I try to keep the reading to an amount that they will consume closely, but confess to being very uncertain what is the modal inflection point for an amount of reading.

[5] It is an undergraduate textbook.

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Week 2: Reality, Perception & Human Knowledge

Week two of my new course, Scientific Inquiry–Theory & Inference,  seeks to provide first year PhD students who want to join the scientific community the basic training they require.[1]  Week one addresses how to succeed in graduate school.  Week two is the first of a two week section on Human Knowledge.  I have titled it Ontology & Epistemology, but that title isn’t great.  This week provides the foundation that justifies the superiority of understanding science as community practice: a set of norms created to solve collective action problems implied by the material we cover in week two.  We structured the course  to make this claim.  And it take an entire semester to present the case.


M.C. Escher’s Relativity, 1953

Ontology? Scientists Don’t Need no Stinking Ontology!

That is correct.  Contributing knowledge claims that achieve high status within a scientific community does not require any reflection about the “real world” and how human’s acquire knowledge about that world.  So why chew up valuable time reflecting on such an arcane topic?

Training students so that they can become producers of knowledge about the processes that generate political regularities is our primary goal.  Achieving that goal does not require that the students invest effort in constructing a logically coherent model of how why science is effective.  The elementary school approach that defines science as a series of steps (a method) pursued by individuals is sufficient.  Imagine, however, that you want also to provide training so that some of them can not only contribute knowledge claims, but also play an active role in (re)shaping the norms and institutions that demarcate knowledge claims that (should) achieve status in scientific communities from those that do (should) not.


I argue in this course that the group (human community) is the scale at which we can best construct an explanation of the superiority of science for the production of knowledge claims that explain regularities humans observe (aka “causal theories,” “causal explanations,” and so on).[2]  Standard accounts of “science,” “the scientific method,” etc. in these courses assume sufficient correspondence between “reality” and human perception of it that we can treat any disconnect as random noise.  That assumption clears the path for a focus on the norms and activities an individual scientist must train herself to adopt and use.  Science has functioned for centuries (and political science for decades) with such an individual focus.

Yet the norms and practices are not static.  The norms and institutions that regulate all human communities are contested.  Sure, we can treat them as static (in equilibirum) over short runs, but they are dynamic as we increase the scale of temporal aggregation we use (covered in week 12).  And take a guess at who I will turn to to help me develop a useful model of the function of norms and institutions?  Not philosophers!  Nor historians!  Nor physicists! Nor biologists, chemists, engineers, psychologists, etc.  Nope.  I turn to social scientists to help me develop such models.

But wait.  I have gotten ahead of myself.  I have not yet explained why we might find value in a model of science that operates (in part) at the human group (community) scale and focuses attention on norms and institutions.  What collective action problem(s) are these norms and institutions helping groups of humans solve?

We can invoke an assumption that the human mind directly perceives reality.  That assumption permits a model of science as “a method” that individuals can be taught to execute.  Indeed, that is a popular model of science taught in elementary and middle school.  Yet an alternative, model of science with much greater theoretical power is available.

What if we conceive of science not as a method practice by individuals, but instead a set of solutions to a variety of collective action problems that standard assumptions about human shortcomings suggest would hobble individual researchers’ ability to execute a method?  We might, for instance, assume that researchers seek to maximize their status.[3]   Such an assumption will readily produce hypotheses about fraud and other problems that bedevil scientific communities.

We might further assume that scientists recognize that individual human beings’ status seeking motive will undermine any attempt to construct a “method” to which individuals should hew.  Each has an incentive to cheat, thereby undermining the collective production of useful knowledge.  To solve the individual v collective incentive clash they might form a community defined by norms and institutions that incentivize individuals to self police and hew to the community norms (effectively by threatening ostracism should one be exposed as a fraud).

The “model” of science I am sketching for my students is quite obviously a social scientist’s model of science.  It is also entirely consonant with what we might call a post modern ontology and a constructivist epistemology.  The primary advantage this model of science offers is direction with respect to thinking about the community’s norms and institutions.  Rather than thinking about “file drawer problems,” “causal identification,” or whatever the flavor of the year happens to be, as isolated issues absent a comprehensive model of science, we now have a model of science with which all social scientists can work as we reconstitute our community’s norms and institutions.  In this model science is a social contract the community (re)produces that define the criteria knowledge claims that warrant intersubjective agreement must meet.  Because there is no “real world” to perceive, describe and understand, but we are capable of producing useful knowledge about social regularities, yet have individual incentives to amass status and advance claims that reinforce our status, we require norms and institutions that will produce that useful knowledge “on average” at the community level.

Note, also, this important implication available from this approach (which is not available to the science as individual practice approach): progress will occur, though the temporal scale for progress is entirely unclear.   No individual study contains “scientific.”  Science is, instead, the norms an institutions we contract collectively.

This week’s  readings make a case for why we might adopt a post modern ontology and a constructivist epistemology, reviewing both some philosophy of science (at an undergraduate text level) and human perception (at a popular science level).  Onward to the reading.


The Assigned Reading

I assign brief video presentation of Wittgenstein’s Beetle in a Box, and then have them read about 2015’s “color of the dresscontroversy, and a brief piece about the human tendency to find patterns in meaningless noise.  I also assign Jonathan Haidt‘s discussion of confirmation bias (pp. 91-7) and Steven Pinker‘s discussion of the perpetrator/victim narrative (pp. 488-92).

These works show us that models which assume that our brain’s filter the information we collect from the world, and that the processes are biased by evolutionary selection that makes our species “fit” into the natural world such that we have yet to become extinct, can explain quite a bit.  Put in more familiar terms, models of human knowledge production that assume that the human brain is a biased instrument vis-a-vis “reality” can explain more of what we agree we are regularities than models that do not.    Those seem like pretty large parts of social life to have no account for.

As for Wittgenstein’s thought experiment, such models of human knowledge production provide no rival account for human language.   The concept “facts” is nothing more than a convention that humans have agreed upon.  That is true of all human language: all human knowledge is constructed, including the models of science we have created.

I also have them read a brief unpublished essay of mine that sketches the differences across pre-modern, modern and post-modern ontological positions, and AF Chalmers (pp. 1–26) and Earl Babbie’s The Practice of Social Research provide more detailed, yet very accessible, accounts (pp. 14-37 and 51-66).

Steven Jay Gould describes the limits the human tendency to focus attention on typical and bizarre outcomes, ignoring the variation between, places on theory building (pp. 44-56 and 77-79).   Arez Aiden & Jean-Baptiste Michel tell a very accessible story about how Zipf chose to look at words differently and the role his conceptual shift plays in providing part of the foundation upon which Big Data is constructed (pp. 26-8, 32-7 and 49-50).  Cohen & Nagel. 1934. “Facts and the Scientific Method,” An Introduction to Logic and Scientific Method pp. 391–2 observe that “nature” does not “imprint” facts on our brains–facts are theory dependent.

All of the reading lays the foundation for establishing the centrality of “intersubjective agreement.”  There are no “facts,” only shared agreement about our understanding of our experiences.  Language like that sends many scientists scurrying for bedrock.  So let me perfectly clear: a model of the success of science that begins by embracing post-modern, constructivist accounts ontology and epistemology is superior to the prevalent (modern) models of science for training PhD students.

Finally, I have tacked on Healy & Moody’s data visualization primer because it kind of fits here, and I want them to be aware that visualization is a topic in its own right.  In seminar I tell them that I believe visualization is not given proper attention in PhD training, and urge them to pursue “self education.”


But They Ain’t Know Nuthin Yet

Yup.  We cover that in week one, and the students are, in fact, first years.  They are learning to bang out “Twinkle, Twinkle Little Star.”  And that makes the reading quite a bit to swallow.

That is why I assign so much undergraduate level reading (Chalmers, my essay, Cohen & Nagel) as well as popular accounts of the issue.  Second, these courses inevitably ask students to “drink from a fire hose.”  Sure, they’ll slake their thirst, but they are gonna get hella wet, and most of the info is splashes to the floor and later evaporates.  But my view is that one wrestles with these issues throughout graduate school, and for many of us, throughout our careers.  If I knew how to do this course in multiple years, and the curriculum committee would approve the courses, I would do it.  But I don’t, and they won’t.


[1]  Nate Monroe and I developed this course together, though our final syllabi are not precise replicas of one another.

[2] We do not discuss scale until the fourth meeting in the Theory section of the course (Week 12).  Welcome to the Gordian Knot that these courses are.

[3] We might further assume that income and other valuable things are a a strong positive function of status.


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File Drawer Exchange on Twitter

This happened on Twitter today, and I think it will make a useful prompt for discussion in seminar later this seminar, so I am recording it here.









Justin Esarey then weighed in with 13 posts explaining why he disagrees (to which I almost tweeted “Given that none of us know what the others are assuming, disagreement seems likely.  :-)” but didn’t), and I imagine the discussion may continue.  Enjoy!


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