Judging Women: Twenty-Five Years Further Toward a Feminist Theory of the State
Bibliographic record
Abstract
This paper engages with the work of Catharine MacKinnon to consider three ways of understanding the phrase "judging women." First, when is it acceptable or necessary to make judgements about what women do? The paper argues that feminist analysis urges compassion and empathy for women, but also highlights the ways that choices are limited and shaped by patriarchy. Thus we cannot and should not avoid all judgment of women’s—and men’s—choices. Second, when can women engage in the act of judging? It is sometimes claimed that it is anti-feminist to engage in such judgment, and that feminists must above all else avoid being judgmental. The paper rejects this idea and argues instead that feminism should insist on women's right to exercise judgment: women’s voices matter. Third, how are we to judge who counts as a woman? MacKinnon’s work offers profound, sustained, rich analysis of these questions, but does not fully resolve 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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.052 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".