On dropping one’s trousers and reclaiming relativism: a reply to ‘The prosecution of Dawoodi Bohra women’ by Richard Shweder
Bibliographic record
Abstract
People do the kinds of things with vulvas and penes that they do with other meaningful forms: they include them directly or indirectly in meaningful gestures—by showing, hiding, touching, or altering their appearance, or by speaking about them—to establish and negotiate and sometimes end relationships, to express or live up to a certain picture of the kinds of persons they are or ought or desire to be, and to make claims about the world. The meanings of semiotic deployments involving genitals are tied to people’s lives among other people, in particular personal and historical circumstances. They are multiple, varied, social, intensely personal, and contingent—in a word, they are relative—as are the moral, aesthetic, and epistemic criteria with which we and others gauge them.
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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 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.064 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.061 | 0.098 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".