What Work of Feminist Legal Scholarship Over the Past Twenty Years Has Been Influential or Important to You, and Why?
Bibliographic record
Abstract
The call for paragraphs generated many different kinds of responses. It was atreat reading the different approaches and having an occasion to listen in asothers reflected on the question. In their own voices, here are a variety of theresponses.Parmi toute la recherche fe´ministe en droit produite au cours des dernie`resvingt anne´es, quel texte a e´te´ le plus important pour vous ou encore, lequel vousa le plus influence´, et pourquoi? L’invitation a` re´diger des paragraphes enre´ponse a` cette question a ge´ne´re´ une grande diversite´ de textes. Ce fut un re´elplaisir de lire les diffe´rents choix et d’avoir l’occasion d’eˆtre a` l’e´coute alors qued’autres re´fle´chissaient sur la question pose´e. Voici un e´ventail de ces re´ponses,re´dige´es chacune dans sa propre voix.I would have to say anything written by Ngaire Naffine, CarolSmart, and Laureen Snider, as their works are provocative, risky,and guaranteed to push your thinking about women, feminism, andthe law onto a whole new terrain.Elizabeth ComackSociology, University of Manitoba‘‘Oh well,’’ said Mrs. Hale’s husband, with good natured superiority,‘‘women are used to worrying over trifles.’’—From Susan Glaspell’s A Jury of Her Peers
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.041 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".