Want Ye Some Building Blocks for Theorizing?

The first four weeks of my Scientific Inquiry–Theory & Inference course covers being successful in graduate school, human knowledge (ontology/epistemology, and what is science?), and week one of the theory section explores the purpose of theory.  Week five provides two building blocks for theory: Concepts / Conceptualization, and Assumptions & Logical Implications.  We establish during week four that the course limits its attention to theory developed to explain why stylized facts occur, which is to say provide accounts of the processes that produce stylized facts.


Framing Discussion; A Historical Digression on Education in the US; and Audience, Audience, Audience

To kickoff seminar I told them that I wanted them to have a 45 minute discussion among themselves (I just listen and take notes) focusing some attention on the ontological / epistemological assumptions they believed the various authors were (implicitly) adopting.  Then I offered what I thought would be a quick digression.

I assign a fair amount of reading from Cohen & Nagel (1934) in the course, and if you have never read it, their presentation is pretty interesting.  And it occurred to me prior to class that I should not assume that many of the students have much context in which to read the work, which is definitely a produce of its time because: Audience, Audience, Audience.

So I explained to them that the book was an undergraduate textbook and that they should bear in mind that Cohen & Nagel could assume that their readers’ high school education drew strongly on the Great Books tradition of the Western canon, especially as taught in New England’s prep schools.  Well, you might think that I would have reflected on the probability that “the Great Books tradition of the Western canon, especially as taught in New England’s prep schools” is pretty foreign to them, and that a non-trivial portion of them may not even know the movie The Dead Poet’s Society (which is, of course, a caricature as well as a morality tale).  But I didn’t.

And a student interjected that she found the authors’ assumption that the reader was familiar with all the stories from antiquity very off putting, and found the book irritating. And I thought: “Teaching Moment.”  And noted that they were in the PhD program to transition from knowledge consumers to producers and teachers and put them in the shoes of Cohen & Nagel by pointing out that they would soon have their own classrooms, and just as the first principle of real estate is Location, Location, Location, the first principle of public presentation of one’s ideas is Audience, Audience, Audience.  And while the “the Great Books tradition of the Western canon” was exclusionary, ethnocentric, etc., it made writing textbooks much easier, and they could really see that in action in Cohen & Nagel.  I encouraged them to reflect, for a moment, upon what they could assume their students would know (pointing out that six years ago, the youngest among them was 12, and probably not terribly engaged in public life beyond popular culture).

Well, hands shot up, and I realized I had shot myself in the foot vis-a-vis discussion of the assigned reading.  D’oh!  But, a teaching moment is a teaching moment, and that there are lots of goals to pursue in this course, and nowhere near enough time to pursue them all (much less do so well).  So I rolled with the moment.




When I took back the reins of discussion after a brief break I began by observing that this week we stood zero chance of covering all of the issues addressed in the reading, much less exploring those that arose during the course of our discussion.  That is a chronic feature of all seminars, but felt especially true that night, given the teaching moment digression and its impact on discussion.

I have them read the first 10 pages of Blalock’s 1969 undergraduate text Theory Construction, and Shively’s undergrad text discussion of the “Importance of Dimensional Thinking.”  That sets them up to consume Barton’s 1955 chapter “The Concept of Property-Space in Social Research.”

Cohen & Nagel (1934) discuss “Terms: Their Intension and Extension,” “The Significance of Classification,” and “Rules for Definitions” (pp. 30-33, 223-33, 238-44), and Bailey discusses classification in his Sage monograph, Typologies and Taxonomies, (pp. 1-6, 11-16).

I have unorthodox views on the state of conceptualization in political science.  I think we stink.  No, that’s too kind.  I believe our work in this area is so poor that students are better off not engaging it.  I do not have the energy to defend that claim here, nor do I during seminar.  So I let them know my view, explain that were they to take the course in most any other PhD program they would either (a) not discuss conceptualization as a distinct building block for theorizing (my guess is this is the modal outcome) or (b) read work that I find detrimental to the field.

To offer some guideposts I identified Aristotle and Weber as arguably the most influential protagonists in our tragedy, and then noted the central role that Weber’s definition of the state plays in my approach to thinking about politics (so they don’t file away “Weber.  Moore hates his stuff.” or something similar, as grad students are wont to do).  I illustrated briefly with a reference to Ideal Type definitions, which rely on checklists, and suffer from both an absence of dimensionality (aka property space) and the problem of negative definition (everything that is not a member of the ideal type is lumped together as an undifferentiated group, unworthy of positive denotation).  Next week I will use Dahl’s two dimensional definition of Polyarchy as a contrast to Ideal Type definitions of Democracy.

Virtually all of the readings identify dimensionality as a central goal of denotative definition.  And Cohen & Nagel discuss the weakness of negative definition.

I then reminded them that we have argued that the course limits its attention to theory as explanations of stylized facts (remember, we still haven’t defined causation–it’s coming soon!).  So my claim is that while Ideal Type definitions have been, and will continue to be, useful for the production of human knowledge, we should explicitly embrace the norms advocated by Cohen & Nagel, Blalock,  and treat concepts that lack dimensionality and/or contain negative definitions[1] as inadequate for the limited purpose of producing theories to explain stylized facts.

I then reminded them that these are unorthodox claims, and were they being trained elsewhere, they would be unlikely to be asked to entertain such views.  🙂

Regrettably, I failed to share with them my claim that any Ideal Type definition can be converted to a useful social science concept (i.e., one with dimensionality) by pursuing these rules (poor time management!).

  1. Provide a denotative definition of your concept.
  2. Name it after the dimension (space) over which cases can be assigned, being attentive to both mutual exclusivity and collective exhaustivity.
  3. Identify whether (each) dimension is defined over ordinal or continuous space, specify the minimum and maximum values, and whether the space between values are ordinal, integer, interval, etc.

If we adopt this checklist as a best practice for concept formation we can understand Ideal Type concepts as conflating dimensions with a dimension’s maximum value.  If we reconceptualize Ideal Type concepts as a maximum value we force ourselves to name the dimension over which the value is a maximum.  Bam!  The negative definition problem disappears as well: we are also forced to provide denotation with respect to the full dimension (property space) and each of its values.  Two birds felled with one tripartite best practice.  Though none of the authors assigned for this section offer the checklist or discuss this issue, it is consistent with the set of them.

Though I failed to offer them this Conceptual Best Practice Checklist, I did remember to explain that dimensionality is valuable because it ensures that we have concepts that can vary.  And in so doing I part company with Clarke & Primo‘s discussion of Models as Maps or, better, placed restrictions beyond “useful for its intended purpose” on the models I want to include in the scientific knowledge community.[2]

If you are thinking “Wait a second.  You are privileging probabilistic theory over deterministic theory (e.g., necessary / sufficient types of explanations),” go to the head of the class!  Yes, I am doing that.  But, I remind you that the discussion of causation is coming up.  The Gordian Knot problem strikes again![3]

I also pointed out to the kids who participated in the Math Camp that we discussed the issues in points 1 and 2 during our study of probability, and reminded them of the distinction between classical probability theory (conceptual) and empirical probability theory.  I then argued that our discipline suffers from a failure to distinguish the two and mistakenly consider these issues more or less exclusively from an operational perspective.[4]   Invoking Jesse Pinkman, “That shit’s conceptual too, yo.”[5]

That set us up for my discussion of typology.  I use a dichotomous distinction between typology and concept.  For me, a typology is any term denoted or connoted by a scholar that has no clearly defined dimensionality (property space).  Though I have not given it adequate thought, I suspect all connotative definitions fall into the typology group of my dichotomy.  Ideal Type definitions certainly do.  And another commonly proposed “concept” in our field does as well: nominal classification schemes.

Bailey would refer to the nominal classification schemes common in political science as unidimensional taxonomies.  I noted that my dichotomous distinction is quite different from Bailey, for whom the most common form of taxonomy is a two dimensional ordered classification that we frequently refer to as a 2×2 classification.  Despite the fact that Bailey’s use of terminology does not map well onto practice in political science or my own dichotomous distinction, I embrace the risk of confusion to impress upon them how much effort other social science fields have invested in conceptualization.  I asked them whether the selection from Bailey had left them with that impression, and received lots of head nods and a few “You can say that again” facial expressions in response.

I then observed that one could retort that any nominal classification scheme could be readily converted to a series of binary “concepts.”  In doing so one would produce a series of Ideal Type “concepts.  As noted above, from there one could adopt the

I used Barbara Geddes typology of authoritarian regimes as my punching bag for illustrating nominal classification schemes.  I choose it because I envy Geddes’ her intellect, and get to tell the class “If I could switch brains with Geddes, I would do it in a moment.  That woman is wicked smart and a really great political scientist.”  That is, I am a huge fan of much of Geddes’ work, and I love having conversations with her.  Just as I point out the enormous value of some of Weber’s work before pillorying his penchant for Ideal Type definitions, I select Geddes’ regime typology because of the extent to which I can laud.[6]

To get that rolling I asked whether anyone was familiar with the classification scheme, and then asked one of those who is to briefly describe the types so that everyone had a flavor for it.  I then asked everyone to identify the number of dimensions (the property space) over which the types could be ordered.  When nobody could come up with a conjecture, I asked whether anyone could make offer speculation on a single dimension over which they might be ordered.  Again, crickets (and the handful familiar with the typology did make an effort).

I then pointed out that just because neither I nor they could identify one or more dimensions over which the typology might be ordered did not mean that nobody could.  Nor did it suggest that Geddes’ authoritarian regime typology was unhelpful for generating knowledge.  Though I doubt any of them have (yet) internalized this, that position is not only dramatically at odds with Clarke & Primo’s position (to say nothing of the huge number of political scientists and others who use the scheme in their work), it is contrary to the postmodern / constructivist understanding of human knowledge that I am advancing in the course.

I reminded them that I am arguing only that I believe we can better (more efficiently, and at a more regular, speedy rate with respect to time) accumulate useful explanations of stylized facts, as defined in this course, if we adopt the Conceptual Best Practices Checklist above.  If we do so, then my proposed distinction between Concepts and Typologies puts Ideal Type definitions and all other nominal classification schemes in the Typology group.  And I maintain that we recognize such efforts as important to the development of human knowledge, but not as useful as Concepts for the production of theories in our scientific knowledge community.[7]

We convey the full argument over the course of the semester.  Hence these posts.


Assumptions & Logical Implications

The second set of readings for the week sketch the assumptions and logical implications building blocks.  The reading for these topics come from Cohen & Nagel (1934): “The Subject Matter of Logic” and “What is a Proposition?” (pp. 3-16, 21-23, 27-30) and “The Function of Axioms,” “The Deductive Development of Hypotheses,” and “Hypotheses and Scientific Method” (pp. 129-33, 197-222).  I also assigned  Becky Morton’s sketch of verbal and formal models (Methods and Models, pp. 33-4, 36-43) and Miller & Page’s sketch of computational modeling (Complex Adaptive Systems, pp. 35-43, 57-62).  Morton’s book is primarily an account of her vision of EITM, but I assign her discussion of non-formal and formal models.  Because formal modeling tends to be reduced to game theory in political science, I also assign Miller & Page, which maps nicely to some of Blalock’s discussion of theorizing (I include an overview of dynamic modeling in the Additional Recommended Reading).  Both books also contain some discussion of the role/value of explicit assumptions and logical implications.

During seminar none of the students raised any of the issues broached in the Morton or Miller & Page reading, and I didn’t manage time well enough to work any in.  But I did ask them for a show of hands if they believed that a social science theory should be logically coherent.  Then I asked them why.


So I challenged them, saying something along the lines of:

Surely you must be able to come up with at least one reason logic is valuable?  Or have you perhaps just accepted its value on authority?  Do you believe that just because teachers and others have told you so for years?

Several students made a stumbling effort to articulate a reason why we should value a theory that proceeds logically from assumptions to implications.  None did terribly well.  There were lots of uncomfortable smiles, as if they knew in their bones that logic was important, but were tongue-tied to say why.

I had planned to lead a discussion about the value of logic from the perspective of intersubjective agreement, from Wittgenstein’s Beetle in the Box, to the preceding discussion of conceptualization, to producing descriptions of the process(es) that explain how and why stylized facts emerge in our collective experiences such that we can reproduce them such that large numbers of humans can recognize them (e.g., datasets, experiments, and so on).  But my prompting query failed to elicit that response.  Who’dathunkit!?!  I don’t know about you, but my prompting queries often strike students more as anchors than life jackets.  ¯\_(ツ)_/¯

I was running out of time, and don’t honestly remember what I threw on the table in a disorganized effort to touch briefly on the several issues I’d planned to cover, but no longer had time to.  I did point out that if one adopts a post-modern ontological position and/or Models as Maps approach, then it becomes nonsense to ask whether assumptions are true, realistic, what have you.  I think I also reminded the students who’d taken the Math Camp about the value of Bayesian updating as decision making in the face of uncertainty, and observed its consistency with/similarity to Cohen & Nagel’s discussion of probabilistic inference.

But I mostly wanted us to bandy about the claim that logical coherence should be the primary criterion we use to trim the infinite number of natural language text strings that might be considered “theories” to a reasonable set that we, the scientific knowledge community, will consider carefully.  Cohen & Nagel (1934) are helpful here with this passage.

The structure of the proposition must… be expressed and communicated by an appropriate structure of the symbols, so that not every combination of symbols can convey a proposition.  “John rat blue Jones,” “Walking sat eat very,” are not symbols expressing propositions, but simply nonsense, unless indeed we are employing a code of some sort (pp. 27-8).

I embellish it with this thought experiment.  Imagine that a monkey bangs out 12 lines of text on a keyboard.  Do we want a criterion that permits us to rule out the “argument” thus produced without having to appeal to the cumbersome process of intersubjective agreement?

Yes.  One of the central claims about the use value of developing a scientific knowledge community is to produce efficient, progressively superior explanations of the stylized facts we observe.  We will discuss what “progressively superior” means in a few weeks.  The Gordian Knot problem never sleeps.  Put another way, we advocate embracing the formal rules of logically consistency and completeness from assumptions to implications as a specific norm that demarcates theory from non-theory on the grounds that it will enhance the efficient production of progressively superior explanations of the stylized facts.

It seems to me to be a common conjecture among political scientists that adopting a post-modern ontological position and constructivist perspective produces “an absence of standards” and makes it impossible to assess the use value of different explanations.  Indeed, some conjecture along these lines is surely modal in our discipline, and likely a supermajority position.  This course challenges that conjecture and embeds the full set of issues presently debated about how we should do science within a common framework built upon the foundation laid in the preceding weeks.



To prepare for class I generally scrawl on the white board wall of my office, take a photo of it with my iPad, and then refer to it during seminar.  Though my handwriting is deplorable, I share those notes here, in case they might prove useful filling in some blanks with respect to my discussion.


Notes for Building Blocks seminar.


[1] These two might well be linked.  I haven’t given it sufficient thought, though surely others have.  I may well have read about this somewhere, and simply forgotten.

[2] I am aware that this Best Practice Checklist rules out constants, for instance.  And truth be told, it is silly to rule out constants as stylized fact by fiat.  Which is to say, if pushed, I will cave on this point.  But few, if any, first year PhD students are prepared to take on that issue, so I am quite comfortable with the hand waving.

[3] And I will tell them then that I leave the deterministic theorizing to others because I am much better at probabilistic theory constructions and assessment of probabilistic hypotheses than I am at deterministic theorizing and hypothesis assessment.

[4] During Math Camp I illustrated the anti-intellectual position of objecting to the study of mathematics by social scientists as akin to objecting to the formal study of musical notation on the grounds that the formalization produces lots of negative outcomes.  As a huge fan of the African polyrhythms, percussion, the blues, rock and roll, ska, reggae, dancehall, DJ, dub, rap, hip hop, and electronica from house to trance to dubstep, I am well aware of the fact that none of these forms owe their existence to formal musical notation, and that the formalization has been used as a tool of culturicide.  Yup, I got all that.  But it just doesn’t follow that the formal language is the cause of oppression, nor that it would be a bad idea to study it and use it to produce music.  I generally don’t like illustration via analogy, but I do like that one.

[5] And here I pull a Cohen & Nagel by assuming that my audience is familiar with Breaking Bad.  #AintNoWayRoundAudienceAudienceAudience  #PopCultureHegemony  😉

[6] I have nothing positive to say about Aristotle’s work

[7] For the record, my examination fields for the PhD were Comparative (major), IR and Methods (minors), and my Dissertation co-chairs (Ted Gurr and Jim Scarritt) self identified as, and were members of, the Comparative field at CU.  I have read virtually every English piece on the so-called Comparative Method published between 1965 and circa 1998 (as well as a good chunk of the Comparative Sociology lit), when I more or less threw in the towel trying to keep up with the Alice in Wonderland world of that exasperating literature.  I also co-taught the Comparative Core seminar at UC, Riverside four or five times during the early to mid-90s.  So, yes, I am aware that these be fightin’ words!  I will also observe, as an aside, that Jonathan Nagler and I proposed, circa 1994, that the UCR Dept of Political Science (where we were faculty) abandon the American, Comparative, and IR fields, and reconstitute the department over two fields: Political Behavior and Political Institutions.  During graduate school I decided that our fields are anachronistic, path dependent nonsense that we would do very well to cast into the sea, and have as yet seen no reason to update that belief.

Posted in Theory & Inference Course | 2 Comments

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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Week 1: How to Succeed in Grad School

As I explain here, this course seeks to provide first year PhD students who want to join the scientific community the basic training they require.[1]  Rather than use the first week of the semester as a chance to “pass out the syllabus” and do introductions around the table, I emailed them these assigned readings a week prior to the semester’s start.


In week 1 I wanted to (a) establish expectations and (b) provide them with tools for success as PhD students.  In seminar I stressed that (i) while PhD study is awesome in lots of ways, it is also stressful and, frankly, shitty a times; (ii) it is unrealistic to expect that your program /college / university will provide all of the assistance and support you want / need (what, with being created and staffed by human beings and all); (iii) unlike the old days when PhD students had to walk 10 miles to school in the snow, without shoes, uphill both ways, blogs and social media exist today and provide ; (iv) your competition is in her first year of graduate school at other universities–the people in this room are your siblings and starting today you individually and collectively want to create a positive, supportive community

In an effort to stress that PhD work is very different than the undergraduate experience, I titled the week “Toto, I’ve a feeling we’re not in Kansas anymore.” All of my US born students knew the quote, but half of my students who were not born and raised in the US had no clue.  D’oh!  I need to be revise that for next time.

The first reading is several sections of Marie des Jardin’s 1994 post How to Be a Good Graduate Student: “Introduction,” “Before You Start,” “The Daily Grind,”“Staying Motivated,” “Getting Feedback,” “All Work and no Play. . . ,” and “Issues for Women.”

I organized the remaining reading into four groups:  “You ain’t know nuthin (yet),” “Putting Critique & Rejection to Work,” “Self Care,” and “Whom might I Follow? #YMMV.”

The first set of readings (and my title, which you will not want to use if you do not have my personal style) are about setting expectations: the degree of difficulty just changed dramatically.  Here are the readings.

Martin Schwartz’s  The Importance of Stupidity in Scientific Research, Kate Bahn’s Faking It: Women, Academia, and Impostor Syndrome, and Stephen Aguilar’s We are not Impostors.


The readings are all about building up confidence and reducing stress, but in seminar I tell them that they have spent 18+ years learning how to consume knowledge, and have all been unusually successful.  Were that untrue they would not be there.  But the next several years are all learning to produce knowledge.  And they have effectively zero experience doing that.  As such they are just like a person learning to play their first musical instrument: everybody stinks when they first play “Twinkle, Twinkle Little Star” on the piano, guitar, what have you.  Nobody wants to listen to you practice or perform!  But there is nothing to be done about it: I stunk when I started, and so did every other PhD.  The difference is that virtually all beginning music students know that they stink, but because traditional (i.e., 20 something) first year PhD students have succeeded in academic work for virtually their entire lives, it would be curious if they thought of themselves as total novices.  The key to the insight is that they are learning to do something entirely new: produce, rather than consume and disseminate, knowledge.  The readings, on the other hand, address the well known downsides to the perfectionist tendencies that have played (and will continue to play) such an important role in their academic success.  The readings also explicitly address systematic variation in the experiences of women (on average) and men, but fails to explicitly address the many other dimensions over which (lack of) privilege influences the PhD experience.  I raise that absence in seminar, but would love suggestions on some posts that address that issue (especially ones that also contain resources).

The second category groups readings that address critique / rejection.  We selected Jessica Weeks’s article Facing Failure: The Use (and Abuse) of Rejection in Political Science, Brian Martin’s Learning to Love Rejection post, and Amanda Murdie’s Rejection post.  For the third set we selected Amanda Murdie’s post Depression and Academia–Let’s Talk, and
Joan Van Every’s How to Take the Weekend Off.

In seminar I did a bit of autobiography, and gave them a thumbnail sketch of my son’s life and death, the fact that I live with depression, that I have an autism diagnosis, and that whatever “life balance” might mean, I am confident I am not a great role model for it.  I then emphasize that my personal story is wholly irrelevant beyond an illustration that, as human beings, academics wrestle with the shite life shovels in our path just like everybody else.  While I have no turn key solutions or recipes to offer, removing those topics from the realm of shame and placing them on the table as legitimate topics for discussion will, hopefully, help us individually and collectively weather those storms and succeed professionally.

Finally, I list a recommended “starter set” of blog and Twitter accounts to follow.  In seminar I emphasized that the key is to curate their own, personalized set of blogs and Twitter accounts, culling and adding to the starter set accordingly.

Twitter: @AcademiaObscura ; @AcademicBatgirl ; @AcademicsSay ; @Dissertating ;
@FromPhDtoLife ; @ISQblog ; @JoVanEvery ; @PHDcomics ; @PhDForum ;
@ProfessorIsIn; @raulpacheco ; @redpenblackpen ; @ResearchMark ; @VivaSurvivors ;
@womenalsoknow ; #ScholarSunday

Blogosphere: Andrew Gelman ; Chronicle of Higher Education ; Duck of Minerva ; Inside
Higher Ed ; Kieran Healy ; Lady Economist ; LSE—Impact of Social Sciences ; Marginal
Revolution ; Math of Politics ; Mischiefs of Faction ; The Monkey Cage ; The Political
Methodologist ; Political Violence @ a Glance ; Quantitative Peace ; Research Whisperer ;
Retraction Watch ; Science of Us ; Sociological Images ; Tenure She Wrote ; Tom Pepinsky ;

While those three categories are not comprehensive, and there are lots of complements and substitutes for the specific reading we selected, it seemed to work quite well.  But the most important things, IMHO, are (a) we began the course acknowledging the (individual and collective) personal  / social aspects and making them legitimate topics of conversation and discussion, and (b) provided them with not just specific resources, but the practice of using social media to build (and share) their own.


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


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Teaching Theory & Inference (aka “Science”) to 1st Year PhD Students

Most political science PhD programs have a semester course for first year students titled something like “Scope & Methods,” “Political Inquiry,” “Research Design,” “Political Science Research,” or a smattering of other types of monikers.[1]  I am teaching that course here at ASU this fall after sitting it out for the past 10-12 years.

Rather than eyeball my old syllabus I decided to start from scratch.  A conversation with Nate Monroe led to a collaboration where we bounced ideas off one another and designed a new course from first principles.  The outcome is a rather different course than anyone has yet put together.

The overarching goal was to kill and bury, without ceremony, the path dependency that dominates instruction.  Tracing the path by which political science has arrived at the community practices of 2016 is of considerable sociology of knowledge interest, but teaching  a course that has a long memoried (auto-regressive, path dependent)  relationship to the syllabus you learned from in graduate school, etc. is not pedagogically sound: it can be understood well as a cost minimizing strategy for the faculty member designing the course, conditional upon ensuring that the students get their “union card” (can signal their membership to professors in the field).

Both Nate and I wanted to narrow the scope of the course to scientific inquiry, which we demarcate in week three, but can roughly demarcate as the pursuit of knowledge claims about the causes of  political outcomes.  Lots of political science research falls outside that scope, and we explain that to the students, and in weeks three and four have some Additional Recommended Readings that help make students aware of the existence of terrain we do not cover.[2]

We set a macro-goal that the course should achieve: to provide the basic training first year PhD students who want to join the scientific community require.  We got together for a couple of days in July and hammered out a sketch of what we need to cover.  This was the outcome.


Over the coming weeks I will (briefly!) explain what and why I am teaching, putting flesh on that skeletal structure.  I hope you’ll find it interesting, and perhaps, even compelling.


[1] The absence of standardization led me to start calling this grouping of course as “Welcome to Political Science” or “The First Course in Political Science”  but nobody seems to find either intuitive.

[2]  During seminar I (have thus far, and will continue to) warn against the unwarranted hubris most who identify scientists (hey, I’m a conflict scholar–you should have expected some peace building).   But that’s a long story, and you should not read this as suggesting that I   The Victim/Perpetrator narrative is ubiquitous to humans on both sides of status hierarchies, and I work my tail off to pull my thinking out of that framing, which assigns “black hats” to “them,”  “white hats” to “us,” and stimulating the all too predictable “lots of smoke, little fire” sniping.


Posted in Theory & Inference Course | 6 Comments