Mere and Partial Means: The Full Range of the Objectification of Women
Bibliographic record
Abstract
Kant discussed the moral wrong of treating people as mere means or as a means only. To treat people as means is to treat them as objects for our use. It is to objectify them. To treat people as a mere means is to treat them wholly as objects, rather than partially so. It is to have an objectifying manner that is absolute or unmitigated. Whether Kant meant to suggest that we commit a moral wrong only when we treat people as means absolutely, rather than partially, is debatable. My concern is with how feminist theorists writing on the objectification of women have followed Kant in emphasizing the extreme case of the mere means. These feminists have implied that the moral wrong of objectification occurs only with absolute objectification, as though between it and respecting someone's autonomy there were no degrees of objectification that are morally suspect. The relevant feminist work centres on such topics as women's reproductive freedom, their sexual freedom, and gender equity in employment.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.075 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".