Tag Archives: Normal Science

Academia, Science, and Anti-Science

Dr. Patrick Thaddeus Jackson’s anti-scientific critique of rational choice theory made me think more of Academia, and its relationship to Science.

Academia and Science are not the same thing. Indeed, for a long time most U.S. government science funding was channeled thru the Department of Agriculture. Many of the great scientific advancements in the United States were likewise made outside the typical academic environment, such as Bell Labs, General Electric, the Manhattan Project, and the Apollo Program. While academia were involved in these places to varying extent, none of them ran on the basis of academic freedom.

How Academia works is not the only way of how Science works. Science already has too many enemies to be dragged down into the political muck with Academics who themselves attack science in addition to creating political enemies. Academia is already under too much attack — such as from teachers union attempting to harvest profits from the public school system – to stay healthy under the anti-Scientific strain.

The proper role of non-Scientific academics is teaching, service, and research that builds useful things. The digital humanities are an amazing and lucrative example of such useful, non-Scientific work in Academia. Jason Heppler of Stanford University runs an awesome blog on such things, Likewise, the cool Geographic Travels blogs emphasizes the utility of spatial and cultural geography. There’s plenty of room for such activity in Academia, too.

But that space is threatened by the anti-scientists — especially elite anti-scientists — who simultaneously attack Science and also generate political enemies. Dr. Jackson’s post titled “The Society of Individuals,” for instance, is an attack on Rational Choice research programs while also attacking politically relevant philosophers for being sexist and morally repugnant.

Science in the Academy is too precious for those who attack Science and the foundations of the Academy. It is a tragedy such parasitic rhetoric is found in the system. It is a waste of resources all around.

A further tragedy is that when non-scientific academics engage in tangential political debates, the (natural) political reaction can be ineffective, counterproductive, and chaotic. Dr. Jackson’s piece is surely an example of the sort of research that Senator Coburn hoped to put a stop to by taking away National Science Foundation support for political science.” But the NSF supports actual scientific work, so the consequences of the defunding are to weaken the Academy, weaken Science, but previously strengthen the voices of those anti-scientific talking heads who might otherwise be drowned out by scientific Academics.

Over at gnxp, Razib Khan has surged that anti-science cultural anthropology “be extirpated from the academy.” More generally, anti-scientists of all types should be too. But there’s no easy or obvious way to do this without risking the Academic Freedom that anti-scientists use to attack science

In conclusion, anti-science should be extirpated from the academy. But I have no idea of how this should be done.

Four Types of Anti-Science

There are scientists, but this post is not about them.

(If you want my career advise for folks who like science, please read the following posts instead: “How Academia Works,” “When It Sucks to Be Young, “Science, Paradigms, and the Old Boys Network,” and How to Escape the Humanities Ghetto.”)

There are people who oppose science in ideological grounds, either out of a specific distaste for science, or else because scientific research or findings leads (or is seen to lead) to objectionable conclusions, or else because they do not know what science is and attack it as part of their other activities.. This post is about them.

Let’s consider two dimensions of anti-scientists, by the nature of their strength.

  • The size dimension accounts for the number of their confederates int their attempt to retard or stop scientific progress.
  • The seriousness dimension accounts for the intellectual rigor and elite infiltration that they and their confederates have gained.

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We can describe each corner of this taxonomy:

  • Popular X Elite: The elite and the public are united against scientific investigation. This is the case in most non-medical human biodiversity research, because of the ideological and historical connotations of such research in the eyes of many. Thus, Human Biomonoculturalists are examples of popular, elite anti-scientists.
  • Popular X Downtrodden: Large, widespread public animosity towards science, but without elite support. In the United States and many Muslim countries, attitudes toward evolutionary biology fall into this category. So Creationists are examples of a popular, downtrodden anti-scientists.
  • Small X Downtrodden: A politically unpopular and generally disenfranchised group is opposed to science, but has not yet gained any form of transaction. So Flat Earthers are examples of small, downtrodden anti-scientists.
  • Small X Elite: A small, highly trained cadre of experts, with elite credentials, attempts to overturn scientific funding. In this post I’ll describe Collectivist Ideologues as examples of small, elite anti-scientists.

An example of such a small but serious attack on science — of Collectivist Ideologues — is Dr. Patrick Thaddeus Jackson’s recent post, “The Society of Individuals,” which appeared at the popular political science blog Duck of Minerva

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The writing in Dr. Jackson’s article is dense, but the argument boils down to the following

1. Rational Choice Theory immorally operationalizes social decisions on the individual, not the society level

So we have two fundamentally different models here: autonomous individuals — prototypical males? — with preferences making strategic calculations, and relationally embedded actors (I’m not going to push the gender point any further here, but I think that many feminists might agree with me about the relative depictions of autonomy-vs.-embeddedness in a patriarchal society) engaged in deliberation and discernment looking for the right course of action. While the former might end up conforming to one or another moral code, only the latter can actually engage in “moral action” per se, because autonomous individuals would be choosing whether or not to act morally while embedded actors would be endeavoring to suss out the moral thing to do and then doing it.

2. The implications of this are morally objectionable twiceover, for being based on individuality and sexism

I still maintain that rational choice theory — and indeed, the broader decision-theoretical world of which rational choice theory constitutes just a particular, heavily-mathematized province — endorses and naturalizes a form of selfishness that is ultimately corrosive of human community and detrimental to the very idea of moral action.

3. Thus, rational choice research programs — and the communication of those programs are “basically corrosive and should be opposed whenever practicable.”

I think that things like Freakonomics [tdaxp excerpt] are basically corrosive and should be opposed whenever practicable. We owe it to the broader society not to simply tell stories that reaffirm the value-commitments and modes of person-hood prized by dominant social actors who want us to equate our happiness with the satisfaction of personal desires

Dr. Jackson’s collectivism idealism states (apparently) that scientists are immoral if they attempt to help control, predict, and improve variation in the world in a way that doesn’t fit with Jackson’s ideals, biases and sentimentalities.

At first glance, Dr. Jackson’s post is odd. It’s too dense and abstract to gain much popular traction. And his description of Rational Choice theory is ridiculous to anyone familiar with it. But such talking heads have wracked havoc in other ares, by attacking science for opposing their sentimentalities and prejudices.

At second glance, Jackson’s post is somewhat more understandable. Political science does not progress like a normal science, and many people who use terms like “Rational Choice” may themselves have no idea how science works. Few anti-scientists are driven by animosity towards humanity. Ignorance of science, and a love of their idealized and wished-for worlds, doubtless plays a larger part.

Anti-science is dangerous. Popular-elite anti-science most of all, but even popular-downtrodden (like the hapless Creationists) and small-elite (like Dr. Jackson’s arguments) should be recognized as the threats to human progress than they are. Human history is a record of one stagnation after another, with brief bursts of progress in between. I hope the anti-Scientists do not stop our current progress, and consign us all to castrated academia composed of ideologues and their pet biases.

The Humanities, the Sciences, and Strategy

The Servants of Strategy

The humanities and the Sciences are siblings. Both serve Strategy. Graduates from the Sciences can usefully serve Strategy to the extent they understand the tools of prediction and control: improvement, and are not distracted by non-normal, revolutionary science. Graduates from the Humanities can usefully serve Strategy to the extend they understand the tools of understanding and explanation, and are not distracted by critical political agendas.

Why We Do What We Do

The purpose of Science is to “predict, control, and improve” phenomena. The sort of phenomenon that is being predicted (at a minimum), controlled (one would hope), and improved (ideally) tells you what sort of Science you are in. Cognitive Psychology focuses on cognitive behavior, “Behavioral” Psychology focuses on overt physical behavior, High-energy physics focuses on the behavior of matter under high energy conditions, and so on.

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The purpose of the Humanities is to “understand, explain, and improve” phenomena. The sort of phenomenon that is being understood (at a minimum), explained (one would hope), and improved (ideally) tells you what sort of Humanities you are in. English Literature focuses on the written works of the English language, Geography on the nature of space, Anthropology on the nature of communities and so on.

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The purpose of strategy is to “understand, control, and improve” phenomena. The sort of phenomenon that is being understood (at a minimum), controlled (one would hope), and improved (ideally) tells you what sort of Policy you are making. Political Strategy focuses on using political influence to obtain and hold offices. Business Strategy focuses on devoting capital and labor to earning a profit. Military Strategy focuses on using violence to achieve political outcomes.

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A Division of Labor

These partially-overlapping purposes make a division of labor sensible. While strategists need to understand phenomenon, they do not need to be able to explain it, thus they can rely on the explanations of others. Likewise, strategists need to control phenomenon, but they do not need to be able to predict it, thus they can rely on the models and planning of others.

Those in the Sciences are useful to the extent they master the tools of prediction and control: tight exemplars, methodology, measurement, and statistics. Those in the Sciences can become useless by being distracted with revolutionary science.

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Those in the Humanities are useful to the extent they master the tools of understanding and explanation, which largely overlaps with the “digital humanities.” Those in the Humanities can become useless by being distracted with political agendas.

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Political Agendas, Like Revolutionary Science…

I’ve written a lot about revolutionary science, so instead I’ll focus on the danger of political agendas in the Humanities. Recently, there have been three articles on the humanities. Michael Berube‘s thoughtful “The Humanities, Unruffled,” Razib Khan‘s philippic Against the Cultural Anthropologists,” Graeme Wood‘s interesting Anthropology, Inc.,” and Megan McArdle‘s stupid “What’s the Use of the PhD?.” In different ways, these four articles all focus on the same two problems:

1. What is the way to ensure that the Humanities PhD fulfills its function of understanding, explaining, and improving society
2. Does “improving” imply a pragmatic or a political objective?

These two questions are interwoven. A pragmatic Humanities ensures jobs for graduates to informing policy-makers, a pragmatic Humanities is fruitful and useful. But a political humanities that focuses on “race studies,” “gender studies,” and so on is simply a predator and parasite on academia, using academic resources to achieve a political objective. Megan McArdle’s post is prety dumb — it’s on the same level of intellectualism as an Afghan hick who dismisses astronomy by saying — but both she and Khan are reacting against the entrenched leftism of the humanities.

What You Do

It’s possible to have a fascinating, rewarding, and fun career in the Sciences or in the Humanities, in academia, in non-profits, government, or in business. Both the Humanities and the Sciences understand the same world, and their purposes overlap in their call to improve the world. How well you learn the tools and avoid the pitfalls of fulfilling these purposes can matter a lot.

Science is Real. Measurement is Real. Improvement Is Real

Bill Gates, the co-founder of the company I work for and a personal hero of mine, has an op-ed in the Wall Street Journal titled “My plan to fight the world’s biggest problems.” It’s an exciting piece because it ties together several of my recent posts very well.

Science allows us to predict, control, and improve variation in the world. In order to actually make progress to these goals, it’s important to establish exemplars of great work. This is enabled through operational definitions that allow concepts to be measured. The quest for progress in science collapses when measurement becomes too difficult tor too expensive.

But the reverse is also true: progress in science begins when measurement becomes accessible.

Bill Gates’ op-ed is so awesome because he brings us back to the real world. When someone says “science,” others thinks of some cartoon view of men in white coats in a laboratory. When someone says that goal of science is the prediction, improvement, and control of variation, someone else will say that such is a “very narrow definition of science, downgrading as it does understanding and explanation.”

But the person who writes you write like Bill Gates does — who never even bother with the word “science” and hammers in that improvements are real:

Such measuring tools, Mr. Rosen writes, allowed inventors to see if their incremental design changes led to the improvements—such as higher power and less coal consumption—needed to build better engines. There’s a larger lesson here: Without feedback from precise measurement, Mr. Rosen writes, invention is “doomed to be rare and erratic.” With it, invention becomes “commonplace.”

In the past year, I have been struck by how important measurement is to improving the human condition. You can achieve incredible progress if you set a clear goal and find a measure that will drive progress toward that goal—in a feedback loop similar to the one Mr. Rosen describes.

This may seem basic, but it is amazing how often it is not done and how hard it is to get right. Historically, foreign aid has been measured in terms of the total amount of money invested—and during the Cold War, by whether a country stayed on our side—but not by how well it performed in actually helping people. Closer to home, despite innovation in measuring teacher performance world-wide, more than 90% of educators in the U.S. still get zero feedback on how to improve.

An innovation—whether it’s a new vaccine or an improved seed—can’t have an impact unless it reaches the people who will benefit from it. We need innovations in measurement to find new, effective ways to deliver those tools and services to the clinics, family farms and classrooms that need them.

… that’s the sort of person who can make a difference. The theory of science, measurement, and improvement are all left below the surface. What is left is a how-to guide to build a better world.

I write this blog for selfish reasons, I enjoy learning about the world. Bill Gates does what he’s doing to change the world.

This Too Shall Pass

The Big Think has a rather poorly worded article, “Can we reach the end of knowledge.”

The article borders are incomprehensibility, because it confuses three things: ways of knowing, which are how we understand the world, science, one way of knowing based on testing falsifiable hypotheses, and normal science, which is a social phenomenon capable of scientific progress through the exemplars of good research.

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Humans will have “ways of knowing” as long as we exist, and science as long as we desire it, so the only sensible way to ask the question is how normal science will end: how will we stop making scientific progress?

Assuming a lack of a nuclear holocaust or other calamity, we will stop making progress in science for the same reason that we will stop making progress in the construction of propeller planes (a technology that has been in decay since the 1940s): the costs will exceed the benefits.

Three broad possible mechanisms for the end of normal science, therefore, are:

1. Increase in the costs of normal science, all other things being equal, or
2. Decrease in the benefits of, normal science, all other things being equal, or
3. Some external change, in other words, all things stop being equal.

On way the costs of normal science might increase is if that non-scientific fields outbid scientific fields for workers whose skills are essential to science. We may already be seeing this happen. A bit ago, Razib Khan had a much better written article, “The Real End of Science,” in which he noted the increase in scientific cheating. This is presumably undetected because there are too few scientists relative to the work we have available to them, and how much we are paying them.

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Related to this, normal science may end because of a decrease in the benefits of normal science. Perhaps the economic return on capital in both the short, medium, and long terms will be relatively low for scientific investments as opposed to capital improvements, and so it does not make sense to pay enough for scientists to engage in research that can make progress.

Thirdly, the ecosystem that supports normal science might collapse, changing the costs and benefits simultaneously. For instance, folks like Diane Ravitch are openly hostile to normal science and the federal-academic complex that supports it. A coalition of leftists and rightists could take down or deform the Large Research Universities and the Grant Funding Agencies to greatly retard normal science, subjecting them to the same lobotomy of low wages that has destroyed the American teaching profession.

Of course normal science will end. The important questions are when it will end, and who will miss it?

The Search for Academic Utility

Over the past few weeks I talked a lot about “paradigms.” Paradigms are “research programs” that focus on a few exemplars of high quality work. This allows science to make progress, and breaks up “old boy” networks by privileging results over connections. The need for progress also allows students to have better lives after they graduate. Professors, like all people, crave money, power and respect.

Thus, normal science, paradigms, research programs, and exemplars align our the need for progress, student’s need for good lives, and professor’s need for professional accomplishment. This is how academia works. Science is not a cartoon. It is a great human achievement that gets human beings to predict, control, and improve variation of the objects scientists study.

Normal Science is good because it is useful, not because it is True.

Consider three areas of work: race-based explanations for school performance, my UFO theory, and the ancient astronaut theory of Great Pyramid construction. If I had to bet, I would bet a great deal of money there’s a very strong impact of race in academic performance not explainable by income, I would bet a small amount of money the aliens at Roswell were from Japan, and I would bet against someone claiming that the logistics of Great Pyramid construction was designed by creatures from beyond the Moon.

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But attempting to found an academic career on either of these theories would lead to a failure to gain tenure. The reason is all are currently outside of the normal science in educational psychology, digital humanities, and Egyptology. None of these theories are currently useful in the field, so none are pursued.

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Normal Science is just part of Science, which along with Inquiry are two of the ways of knowing about the world. There’s more to this world than captured in data sets. My friend Mark Safranski recent captured readers after linking to a data set, stating “ there are hidden qualitative decisions in who did the counting, how and by what yardstick.” Indeed, Normal Science has even more limitations than that.

A lot of grief is caused by considering Science the search for Truth. It may that, but Normal Science is the search for utility in an academic context.

Controversies in Normal Science

tl:td: “Normal Science” refers to science when scientists focus on making progress, not just arguing in circles. The difference between probabilistic and bayesian — or “go-up” and “go-next” — statistics matters much more to the future of normal science and the nonsense that Walt and Meirsheimer came up with in a recent article.

Will F. Moore, at his blog A Second Mouse has a really funny up titled “Commentary on Mearsheimer and Walt.” Not surprisingly, it is a commentary on Mearsheimer’s and Walt’s (d’uh! :-) ) recent post, which I also criticized. Basically, Mearsheimer and Walt wrote a piece in which they demonstrated deep confusion of scientific methods, and lamented the decline of the “old boys network” and its replacement by objective methods of evaluation.

One of the ridiculous parts of Mearsheimer and Walt’s columns is their inability to distinguish substantive from non-substantive divisions in normal science. For instance, a large part of Mearsheimer and Walt’s piece is dedicated to a discussion of “scientific realism,” which appears to be a confused discussion of instrumental validity. Mearsheimer and Walt completely miss the division of scientific research into frequestist and bayesian camps, which Will Moore humorously emphasizes:

Quantitative approaches—particularly the misapplication of hypothesis testing methods which make complete sense in the context of survey research but no sense whatsoever in the context of the analysis of one-off populations—may be wrong, but at least we can systematically say why they are wrong.[4] Grand theory?—welcome to the narrative fallacy and that wonderful little hit of dopamine that your brain gives you in response to any coherent story. And that’s all they’ve got to work with.

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[4] And again, we know of plenty of alternatives, including the rapid emergence of Bayesian model averaging which is likely to wipe out the cult of incremental frequentist garbage can models. The cult otherwise known by the initials APSR and AJPS.

Previously on this blog, I’ve referred to “frequentist” and “bayesian” statistics as “go-up” and “go-next,” because frequentist work tends to emphasize building a model of reality, while bayesian models tend to focus on predicting what will happen next. As I wrote previously:

The Go-Up view of statistics is that statistics measures the population from which an observation comes from. The appropriate way to go-up is to wait until you have a sufficient number of observations. and then generalize about the population from our observations. So if you are conducting science, and you notice. This is the method that Derbyshire was describing in 2010. A large number of observations of academic performance show consistent gaps between black and white learners. Because we’re “going-up” from observations to populations, we can conclude some things about the population, and how outcomes in the population should work-out over all, but it makes no sense to try to predict any given student’s success based on this. We’re going-up, not going-next.

The Go-Next view of statistics is that statistics gives us the likelihood of something being true, based on what has come before. In Go-Next statistics, population-averages are besides the point. What matters is guessing what’s going to happen, next, based on what you’ve seen before. The whole point is to guess what’s going to work for individuals you know only a few things about, based on your experience with other individuals who shared some things with the new strangers.

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The superstructure of science changes as the infrastructure of the economy changes. The Go-Next philosophy of statistics, once the peasant stepchild of the serene Go-Up interpretation, now reigns supreme.

The unfolding victory of Go-Next Statistics matters much, much more than, say, the Copernican Revolution. The number of people whose daily conversations were actually impacted by Copernicus may have been a few dozen, all involved in the Papal-Academic complex.

How many times a day does Facebook’s decision of which news to share impact you?

There are real controversies and real research programs in normal science.

Too bad Walt and Mearsheimer know so little about normal science they were unable to identify either.

Money, Power, and Normal Science

Fabio Rojas has a post up titled Theory Death in Political Science. It links to a post by Stephen Saideman, “Leaving Grand Theorists Behind,” which was published as Saideman’s Semi-Spew. (A companion piece was also published at Duck of Minerva and discussed by me earlier.)

Here’s the beginning of the post:

A definition: theory death is when some intellectual group tires of theory based on armchair speculation. Of course, that doesn’t mean that people stop producing theory. Rather, it means that “theory” no longer means endless books based on the author’s insights. Instead, people produce theory that responds to, or integrates, or otherwise incorporates a wealth of normal science research. In sociology, theory death seems to have happened sometime in the 1980s or 1990s. For example, recent theory books like Levi-Martin’s Social Structures or McAdam and Fligstein’s A Theory of Fields are extended discussions of empirical research that culminate in broader statements. The days of endlessly quoting and reinterpreting Weber are over. :(

Now, it seems, theory death is hitting some areas of political science.

What Fabio Rojas calls “theory death” is the “normalization of science.” That is, the establishment of methods that allow for progress in the prediction, control, and improvement of behavior of some object of study (molecule, person, State, etc.) over time.

The next line is particularly important:

Science becomes normalized when the power the Old Boys network achieves through limiting competition is overtaken by the money available for creating progress.

There have been two great flowerings of science in American history. Both emerged from the establishment of the great American University System in the late 19th century, but they accelerated at different times. As I wrote previously:

Following the Second World War science boom, the federal government accelerated the rise of the American research universities. From the Second World War to the Vietnam War, physics was a favorite area for funding. From this we received many new physical inventions, such as a transistor. After the Vietnam War, medicine is a favorite area for funding. Now we have great medical breakthroughs.

While social science research funding is only a fraction of medical research, the federal academic complex ensures that there is bleed through from health sciences to the social sciences as well. The bureaucratic momentum for peer-reviewed scientific research funding. Such funding requires that researchers seek to achieve progress in some areas, which of course privileges normal science (which is capable of achieving progress) relative to non-paradigmatic science (Which is not).

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The reason that Political Science is late to normalization — why it is experiencing “theory death” later than other fields — come from the obvious exception to this general rule for how academia works:

Professors, like most people, respond to the incentives of power, influence, and money.

The institution of tenure reduces uncertainty regarding money, and focuses the incentives on power and influence.

Power in academia comes from the number of bodies a professor has under him. These bodies might be apprentices (graduate students he advises), journeymen (post-docs who have a PhD and work at the lab, or staff researchers), or simple workers (lab technicians, etc).

Influence in academia comes from the extent to which one is successful in influencing one’s peers. This is typically measured in terms of influence scores, which are a product of how often the academic is cited, weighted by how important of a publication he is cited in.

Unlike other places in academia, professors hope to influence national policy makers, and so are relatively immune to academic discipline. This actually hurts scholarship. For instance, Victor Cha’s otherwise great book on North Korea, The Impossible State, is pretty much ruined by his analysis of Kim Jung Il, which was basically a job application. Likewise, Stephen Walt and John Mearsheimer (who began this discussion by defending the Old Boys network) basically produce political propaganda for the Old Right (pessimistic, Army-focused, and anti-Zionist). The lack of academic discipline has allowed political science to get away with graduating students into the “humanities ghetto” — because skills don’t matter in political science as much as connections, those without connections are left with high unemployment and bitter job prospects:

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The way forward is probably for grant-funding organizations to support normal science in political science research, and for political agitators to coalesce within agenda-driven “think tanks.” Educational sciences have already experienced this split. It’s time for Political Science to normalize, too.

Science, Paradigms, and the Old Boy Network

On Facebook, Daniel Nexon pointed me to this post by Steve Saideman, titled “Lamenting The Loss of the Light, The Ebbing of Grand Theory and The Decline of Old Boy Networks.” Saideman’s post itself is a commentary on Stephen Walt‘s and John Mearsheimer‘s ridiculous article, “Leaving Theory Behind: Why Hypothesis Testing Has Become Bad for IR,” which will soon appear in the European Journal of International Relations.

Walt and Mearsheimer’s article is absurd on many levels. But I mention it for how well it reflects my post, “Progress, Science, and Exemplars — or — When It Sucks to Be Young.”

In that post, I mention that it is horrible for your career to be young in a science with loose exemplars — that is, in a field that is “non-paradigmatic” or a “revolutionary science.” The more revolutionary the science, the looser the exemplars, the more “knowledge” and “experience” are both measured in years. The less things change — the less progress is made — the less youth matters relative to years of experience.

Or in diagrammatic form:

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What’s bizarre is that Walt and Mearsheimer agree with me! But this makes them sad. Walt and Mearsheimer would rather political science stay as anti-youth and revolutionary as possible, so that their power and influence could remain strong:

Over time, professions also tend to adopt simple and seemingly objective ways to evaluate members. Instead of relying on “old boy” networks, a professionalized field will use indicators of merit that appear to be impersonal and universal. In the academy, this tendency leads to the use of “objective” criteria—such as citation counts—when making hiring and promotion decisions. In extreme cases, department members and university administrators do not have to read a scholar’s work and form an independent opinion of its quality; they can simply calculate the “h-index” (Hirsch 2005) and make personnel decisions on that basis.22

The second part of the paragraph is literally incoherent, attacking the use of an h-index by arguing it’s a raw count of citation. Walt and Meirsheimer seem unable to do math, and so their inability to understand even basic fractions should not surprise you. What should be surprising is they are so openly defending the power aristocracy that comes from using subjective scores and the “old boys” network!

In fairness to Walt and Meirsheimer, the intellectual poverty they confess through their incoherent ramblings is not entirely their fault. Political science has been so revolutionary, so paradigmatic, so subjective for so long that few may know what a science actually is, or even understand the terms used to describe science.

Consider this earlier passage in Walt and Meirsheimer’s article, in which the “worse than wrong” passage is intended to be uncontroversial:

Indeed, some senior IR scholars now rail against the field’s grand theories. In his 2010 ISA presidential address, for example, David Lake described the “isms” as “sects” and “pathologies” that divert attention away from “studying things that matter” (Lake 2011: 471). Thus, it is not surprising that “the percentage of non- paradigmatic research has steadily increased from 30% in 1980 to 50% in 2006” (Maliniak et al 2011: 439). Of course, one could advocate for middle range theories while disparaging grand theories, and indeed Lake does just that. The field is not moving in that direction, however. Nor is it paying more attention to formal or mathematically oriented theories (Bennett et al 2003: 373-74). Instead, it is paying less attention to theories of all kinds and moving toward simplistic hypothesis testing.

The highlighted passage, originally by Daniel Maliniak simply means that empirical research is increasing, and that non-empirical research is declining, within political science. But Maliniak, and thus Walt and Mearsheimer, bizarrely use “paradigmatic” to refer to less paradigmatic (that is, less capable of progress) fields, and “non-paradigmatic” to more to more paradigmatic (that is, more capable of progress) fields.

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Political science has been in the fever swamp for so long that the notion of progress as an outcome of normal science has almost entirely been lost. If Walt and Mearsheimer had their way, it might be lost, and the field simply divided into a stationary oligarchy of old boys network.

At one point in their article, Walt and Meirsheimer say that “the creation and refinement of theory is the most important activity in [social science].” This is nonsense. The most important activity in science is the prediction, control, and improvement of behavior. Theory can help, diagram can help, interviews can help, process tracing can help. But the paen to old boys network, and the nonsense that Walt and Mearsheimer try to pass off as a scholarly article, certainly doesn’t.

Progress, Science, and Exemplars — or — when it sucks to be young

Some people divide the ways we know about our world into two types, Science and Inquiry. Science typically refers to using falsifiable hypotheses to make predictions about the world. Inquiry refers to any deviation or alteration of this method.

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For the rest of this post I’m going to talk about fields in which the objective is to control, predict, and improve the behavior of some object (cancer cell, human being, State, whatever). That is the purpose for which the tool of science is most applicable.

Some people further divide Science into two types: Normal Science and Revolutionary Science. These terms from from Kuhn’s book, The Structure of Scientific Revolutions. Normal Science, in Thomas Kuhn’s original model, was capable of progress but governed by religious-like “paradigms.” Revolutionary Science, likewise in Kuhn’s outdated model, was capable of freedom but incapable of progress.

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I say “original” and “outdated” because no one — except for pretentious modern literature types, and including Kuhn himself — takes that model seriously anymore. While The Structure of Scientific Revolutions was a breakthrough at the time (because it implied that science was not completely free, and that not all science would yield progress), the feedback to the model was intense and Kuhn’s model of science rapidly improved.

Instead of two distinct types of Science, Kuhn’s revised models described any scientific field as having “exemplars,” or examples of how the best research is conducted. Some fields (like structural equation modeling, say) have exemplars which are very similar and allow creativity only within that narrow and defined space. These “Normal” fields are capable of rapid progress. Other fields (like political science, say) have exemplars which are so wide and dispirit that researchers can basically do anything they want, and progress is extremely difficult.

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The looser the set of exemplars, the more role there is for “inquiry” within the science. For instance, take my own field (Educational Psychology). My dissertation was a mixed methods inquiry that involved a substantive literature review that stretched back to the 1970s as well as qualitative interviews with participants. That sounds a lot like inquiry and non-science. But my methodological section involved a literature review that went only back to 1999, with most of the work having been published within just a few years of my dissertation. That sounds a lot like science and progress.

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One way this matters is that in less-progressive, more scientific, looser-exemplar, fields, “knowledge” and “experience” are both measured in years. The less things change — the less progress is made — the less youth matters relative to years of experience.

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The worse your bargaining position as you start in life, the more you find yourself without experience in an experiential field, the harder everything is. In some antiquated and retrogressive societies, workers with poor negotiating position are even told who they may and may not marry.

Of course, it’s possible for the young to do well in less progressive fields of study, as the old may do well in more progressive fields of study. It’s just that the field is never balanced. Experience pays, and the level of progressive in the field determines how much.