1. Always decide the day before what you will write and when you will write, one key sentence and 27 minutes at a time.

2. Never write about something you just learned this week. Always write about something you knew last week at the latest.

3. Always write a single paragraph of at least six sentences and at most 200 words in support, elaboration or defense of a single well-defined claim expressed in the key sentence.

4. Never write a paragraph that you have not planned the day before. Never write at a time you did not plan to.

5. Start on time and finish on time. If you start late, still finish on time.

6. Always take a three-minute break after writing the paragraph. In this break you must do something that is not related to either your writing or the rest of your day’s tasks.

7. Do not write from your sources. Write from your notes or from your memory.

8. Do not leave “chores” like proofreading and referencing “for later”. They are part of the activity of writing the paragraph for 27 minutes.

9. Read your paragraph out loud sometime in the last five minutes of each 27-minute writing moment.

10. Do not write more than six paragraphs per day. That is, do not write for more than three hours each day.

11. Do not render any absolute judgment on your paragraphs. At most once a week, simply rank them from best to worst.

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*To be followed by the scholar seeking to become a better prose writer during eight weeks (40 days) of deliberate effort directed to that end.

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Ezra Zuckerman recently pointed me toward a comment he wrote with Catherine Turco on Duncan Watts’s critique of interpretative or “empathetic” (as opposed to explanatory or “causal”) approaches to sociology . We had been exchanging views about the replication crisis in the social sciences, and Ezra suggested that one of the reasons our theorizing has run wild is the assumption that the mechanism we posit to explain phenomena need not be intuited. In the comment, he and Turco put it as follows:

…our lack of intuition for the mechanisms means that the sole basis for acceptance of such research lies in the results that are presented. Empathetic theories do not rely solely on empirical validation but also on how plausible it is that reasonable individuals would act in the manner supposed by the theory; this sets a higher bar for acceptance of the theory independent of empirical results. (p. 6)

I’m not sure people have to be presumed to be “reasonable” in order for their actions to be “plausible”. It’s long been my view that humans are as distinctly passionate as they are reasonable and that their plausibility, therefore, is as bounded by their passions as their reasons. (Note, indeed, that we’re talking about “empathetic theories”.) But the general point that they are making here is a strong and important one: whatever mechanism is proposed must make intuitive sense.

Another way to put this is that we must be able to imagine it. We must be able to “make ourselves pictures of the facts,” as Wittgenstein famously put it, except that these are not the “cold” facts of natural science. They are facts with which we are also intimately familiar. A description of the mechanism must be recognizable to us, as it were, from the inside. It is not merely something that explains certain effects, but resonates within us as that which moves and is moved by things.

Ezra and Turco emphasize that there’s a substantive issue here:

There can be no debating Watts’s premise that the two modes of inquiry and associated standards are distinct. As he points out, the physical sciences operate purely in the causal mode. Physical scientists do not find it productive to imagine what it would be like to be an electron or cell in order to explain its behavior.

This reminded me of Hayek’s suggestion in the The Counter-Revolution of Science:

The physicist who wishes to understand the problems of the social sciences with the help of an analogy from his own field would have to imagine a world in which he knew by direct observation the inside of the atoms and had neither the possibility of making experiments with lumps of matter nor opportunity to observe more than the interactions of a comparatively few atoms during a limited period.

In a certain sense, physicists do imagine “what it would be like” to be an electron or cell. It’s just that they imagine it would be a very simple and utterly causal (stimulus-response-type) experience. They don’t imagine that the cell or electron would make up its mind about its behavior, not even retrospectively, and this would-be act of sensemaking is therefore quite understandably–quite plausibly, if you will–left out of account in the explanation.

But in the case of human behavior we have to imagine people behaving in one way or another with some awareness of what they are doing. They must, at the very least, live with what they have done. They are not merely gears grinding inputs into outputs. They are conscious beings. Moreover, given our (admittedly somewhat intermittent) empathy with them, we aren’t able to throw them together in all manner of “experimental” situations to test their limits. Social experiments are constrained by our ethics, and those ethics are, presumably, also already constraints on the human behavior that our experiments are designed to help us understand.

There is too much to say about all this in a single blog post. I will take up Ezra Pound’s idea that “the arts provide the data for ethics” in another post. What I wanted to emphasize here is that there can be no knowledge of things without imagination. When it comes to a cell or an electron we have no need to imagine its “inner” workings (at least not in the sense of their subjectivity), but when we turn our science on each other we cannot rest until we have proposed a mechanism that makes intuitive sense of our own lives. Otherwise we end up with a social theory that requires us to be nothing more than pigeons or worms or, indeed, cells or electrons. Like I say, that may already be ethically inadequate, but it is certainly counter-intuitive.

“Every theory is a program of perception.” (Pierre Bourdieu)

I recently stumbled on the DoctoralWritingSIG blog (HT Julia Molinari). “What does it mean to ‘theorise’ research?” asks Cally Guerin in a recent post . This is something I happen to have a lot of ideas about, so I posted a quick comment about what  I tell students and researchers: “Theory” is really just the expectations you share with your reader about your object, or at least the expectations you shared with them before you analyzed your data. In your theory section, then, you are setting your reader up for an artful disappointment. You are reminding the reader of what they expect, well aware that what you have found will challenge those expectations and therefore occasion learning.

In classical hypothesis-testing approaches the theory would serve as the basis for constructing the null. The hypotheses that are constructed are normally sought to be confirmed, not disappointed. That is, the “artful disappointment” comes from the rejection of the null, not the hypotheses. Though I don’t pretend to be an expert here, theory can, as I understand it, be used in a similar way by Bayesians (like Andrew Gelman) to construct the “prior”. In both cases, “theory” includes the empirical results of past studies, which allows to estimate effect sizes. That is, theory is not just a set of causal laws, but also some generalized initial conditions.

But a “softer” approach to theory-as-expectation can also be taken to qualitative research. The theory provides a schema of expectations. So, by announcing that you are deploying, say, Genette’s theory of narrative, you are fostering an expectation that the analysis will identify the order, frequency, and duration of events, and provide an account of the voice and mood of the telling. The theory section will be written with a presumption that the reader is familiar with Genette’s narratology, and with what past applications have found “works” (and does not work) in particular narratives. As in statistical analysis, the theory gets us to anticipate anticipate “effects” in the material to be analyzed.

Given only the theory and description of the data (in the methods section) the properly trained (“peer”) reader should be able to form a qualified opinion about what the analysis will show. That opinion should, preferably, be shown to be incomplete in the paper. But I should stress that the importance of publishing null results is becoming increasingly clear in many fields.

In fact, I need to rethink this view of nulls, priors and expectations in light of the growing “replication crisis” in social science. An important guiding insight here, which has been with me for a long time, has come from Ezra Zuckerman, who emphasizes the importance of constructing a “compelling null”. And here we have to keep in mind that what we find compelling is always changing. So, in the early days of research into so-called “priming” and “implicit bias”, the null was supposed to be that such effects did not exist. Today, however, the orthodoxy (albeit one that is somewhat besieged) is that such biases do affect our thinking. Now, the question is: how much and under what conditions?

The general point, I dare say, still holds: our theory structures our expectations of our object. And those expectations are shared, communal. Our research, likewise, should be designed to challenge those expectations, but they should probably be published even where their challenge fails and the expectations hold. I guess there’s a sort of meta-theoretical twist here: although our theories tell us what to expect, we also expect that all theories leave something to be desired. In a sense, we expect to be disappointed. Some people, and especially students, sometimes forget that and let the disappointment, when it inevitably comes, frustrate them. What has actually happened, like I say, is that they learned something.

“And what is good Phaedrus, and what is not good — Need we ask anyone to tell us these things?” (Socrates, as used in the epigraph to Robert Pirsig’s Zen and the Art of Motorcycle Maintenance)

Nothing has been subject to greater mystification than the notion of “quality” in writing. It is ironic, perhaps, that Robert Pirsig’s famous novel has inspired generations of writers to think that it is impossible to say what makes a text good or bad, or even, perhaps, whether there is any such thing as good and bad writing. Pirsig’s point, after all, was, not that we can’t talk about these things, but that we don’t really have to. Though the virtues of a text are many and varied, the quality of a text is obvious. We don’t need to ask anyone to tell us about it. It’s right there on the page of a well or badly written text.

Consider the art that Pirsig’s title alludes to. Though his book is arguably more famous and, for many, the original exemplar of the phrase “zen and the art of…”, it was of course derived from the title of a book that was more famous at the time: Eugen Herrigel’s Zen in the Art of Archery. The archer either hits the target or not, or gets some measurable distance near it. There is no mystery about what it means to be a “good” archer, though there is much artistry in the process of becoming one. The spiritualism and mental illness of Pirsig’s hero notwithstanding, his message is very similar when it comes to writing. Don’t let someone else tell you whether you hit the mark. Pick a target and try to hit it. “Be the arrow”, if you must, but don’t be in doubt about what you are trying to do.

For a long time now, I’ve been engaged in trying to demystify the problem of prose writing. And I’ve come to the (not very original) conclusion that the greatest obstacle to progress is the attitude to writing that is cultivated in our schools. Students are learning to “do assignments”, not to write well. Nothing similar happens in athletics or music or art, where “quality” remains a familiar result of mastery. Even someone who is not good at playing the piano, or running 100 meters, or drawing a hand, is able to recognize a competent attempt when they encounter it. We are all able to be immediately impressed at these things.

I assert that the same is true of writing. We can evaluate the quality of a piece of writing independent of context and content just as easily as we can detect a good pianist independent of whether we like the music or feel it is appropriate to the occasion. (I personally think Leonard Cohen’s “Hallelujah” is a beautiful song but completely out of place in a church service, no matter how beautifully it is sung.) We can, and often do, acknowledge that a particular writer is very knowledgeable about a subject but has little control over their prose. We know how to distinguish a good gymnast from a good basketball player. We may not know much about art but we do, in fact, know what we like.

We have to return to this basic, immediate appreciation of quality in writing. We have literary sensibilities that we don’t need anyone to establish for us. But, like any other sensibility, we can certainly sharpen it. The problem should not be “What is good writing?” but “How can I write better?” There is work to be done. Pirsig was probably right to say that the real machine you’re working on is your self. But that should not make the process of gaining mastery more mysterious. It’s the most familiar thing you know.

This is a wonderfully lucid (and somewhat surprising) discussion of the role of abstraction in research. Chomsky reminds us that mathematics isn’t literally a language. The sentence I’ve used as my click-baiting title comes in his very clever analogy between (I presume) long jumping, chicken flight, and the flight of eagles. (It starts at 4:35.) He points out that people can jump about 30 feet (the record is in fact under 25 feet) and chickens can only manage about an order of magnitude (about 300 feet) more than that. Eagles, meanwhile, can stay in the air for hours.

It’s true that humans fly more or less like chickens and neither are like eagles. But that’s not the way it works. Chickens fly like eagles and humans don’t fly at all.

That is, humans “fly” (metaphorically) almost as well as chickens, but eagles (literally) fly much better than chickens. Similarly, we might compare pidgin languages, i.e., “chickens” (not “pigeons”) with Eagle … sorry, English. One is more of a language than the other but both are, as Chomsky points out, natural phenomena. Mathematics is a deliberate human invention. So, it’s true that mathematics is a language more less like pidgin and neither is like English. But that’s not how it works. A pidgin is language like English and mathematics isn’t a language at all.

(I’m happy discuss my stretching of this metaphor and the quality of my puns in the comments.)

You may have been struck by Chomsky’s matter-of-fact denial of a meaningful concept of the “physical” as something distinct from the “mental”, saying there just is what there is and some of the things there are are thoughts. I like that way of approaching it. It’s an approach to a problem I took up a while back about the facts and “propositional states”. It’s sort of liberating to just talk about facts and things how they are related and not worry about thoughts and concepts as some weird other kind of “stuff”.

We must recognize that all our thinking about things, and certainly all methodical inquiry, is really about “abstract objects”, not things as such, separate from our mental life. The things are simply part of our mental life. Idealization, as Chomsky points out, thereby gets us closer to reality by making us more precise. It lets us understand specific aspects of the world more clearly. By the time we can apply a mathematical formula, we’ve made things sufficiently simple, very precise, that’s all. I think there’s much to learn from this.