Bibliographic record
Abstract
Abstract. I argue in this paper that there are certain similarities between Catharine MacKinnon, on the one hand, and Mary Wollstonecraft and Jean Jacques Rousseau, on the other, in the conception of inequality and its origins. All three make two important claims that characterize their accounts of inequality: first, that inequality is not natural, and second, that the differences which are alleged to justify inequality are in fact produced by the inequality. These two claims distinguish one way of arguing for equality. I contrast this with another way of arguing for equality, one which acknowledges natural differences. Résumé. Dans ce texte, je soutiens qu'il y a des similarités entre les conceptions de l'inégalité et de ses origines de Catharine MacKinnon d'une part et de Rousseau et Wollstonecraft d'autre part. Tous trois soutiennent deux thèses importantes qui caractérisent leur conception de l'inégalité : premièrement, l'inégalité n'est pas naturelle; deuxièmement, les différences qui sont invoquées pour justifier l'inégalité sont en fait le produit de cette inégalité. Ces deux positions sont distinctives d'une ligne d'argumentation défendant l'égalité. Je contraste cette ligne d'argumentation avec une seconde façon de défendre l'égalité qui diffère de la première en ce qu'elle reconnaît les différences naturelles.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".