Bibliographic record
Abstract
The practices of polygyny and bound feet, now extinct in China, once held in common an underlying structure of perversion. In novels of the Qing dynasty, the man was perverse when he acted as if having multiple wives was not something he desired or initiated, but was something that arrived to him because of external conditions. In particular, women were the agents of polygynous marriage, not the passive participants, because they allowed and encouraged the man to take other wives. I call this form of marriage passive polygyny, where the man passively accepts polygyny. The underlying link between passive polygyny and bound feet has to do with the same logic by which the dominant subject appears to hand the production of a situation – polygyny or bound feet – to the ones who are dominated or, in the case of bound feet, mutilated. Women mutilated themselves and their daughters, but they did so of their own will, by their own methods, and on their own schedule. In households across China from the Song dynasty to the early Republic, mothers initiated and managed the binding of their daughters’ feet. With a bit of modification, the psychoanalytic definition of perversion describes the deep structure of both polygyny and footbinding, and does so in powerful and compelling terms that no one has yet begun to unravel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.023 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".