Bibliographic record
Abstract
Kai Erikson wants to give good reasons why sociologists don't write as well as the author of Ecclesiastes or as Abraham Lincoln.Well, OK.Even a sociologist who writes as well as Kai himself doesn't have to compete with that kind of prose, largely for the reasons he gives.We don't aim at eloquence.Most of us want, instead, to inform our readers and persuade them that our ideas and findings will stand up against skeptical critics rather than uplift them or persuade them to engage in political action (well, most of us, anyway).We want, as he says, "to convey ideas and information with enough clarity to be easily understood outside the narrow precincts of the discipline and yet with sufficient precision to allow for careful inspection and evaluation within it."I don't quarrel with that statement of what sociological writing ought to achieve.But is our failure to compete with Abraham Lincoln's eloquence really the problem with the writing that appears in our professional journals and books?It's true that back in the old days such forgotten literary types as Malcolm Cowley used sociology as the bad example of academic writing.But now, as Kai notes, critics have the professors of literature and cultural studies and allied fields to kick around, and don't badmouth us so much anymore.The problem of sociological writing is still what it was when the discipline was younger: how to write clearly and precisely (and, of course, the two overlap).We have to write, as best we can, so that readers can follow our arguments and know what we are talking about, so that in principle they could produce a paraphrase of what we said and we would say in return, "Yes, that's exactly what I meant."When we mean X we have to say X so clearly and precisely that no reader can think we meant Y.We have to specify what we are talking about so that readers can see that we are not talking about three other things with which our topic might be confused.That's the problem of scientific writing, including writing in the social sciences.Clarity and precision aren't complicated requirements, but they're not nothing.It takes a lot of care and some skill-not an enormous amount, but some-to put together sentences, paragraphs, and chapters whose point a reader won't misunderstand.To do that, we have to define our terms carefully and make our concepts clear.But it's not easy.Thomas Kuhn, as careful and skilled a writer as the social sciences ever produced, managed to use his key term, "paradigm," in some 20 distinguishably
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.219 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.030 | 0.024 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.021 |
| Insufficient payload (model declined to judge) | 0.032 | 0.098 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".