Teaching with Feminist Judgments: A Global Conversation
Bibliographic record
Abstract
This conversational-style essay is an exchange among fourteen professors — representing thirteen universities across five countries — with experience teaching with feminist judgments. Feminist judgments are “shadow” court decisions rewritten from a feminist perspective, using only the precedent in effect and the facts known at the time of the original decision. Scholars in Canada, England, the U.S., Australia, New Zealand, Scotland, Ireland, India and Mexico have published (or are currently producing) written collections of feminist judgments that demonstrate how feminist perspectives could have changed the legal reasoning or outcome (or both) in important legal cases.This essay begins to explore the vast pedagogical potential of feminist judgments. The contributors to this conversation describe how they use feminist judgments in the classroom; how students have responded to the judgments; how the professors achieve specific learning objectives through teaching with feminist judgments; and how working with feminist judgments — whether studying them, writing them, or both — can help students excavate the multiple social, political, economic and even personal factors that influence the development of legal rules, structures, and institutions. The primary takeaway of the essay is that feminist judgments are a uniquely enriching pedagogical tool that can broaden the learning experience. Feminist judgments invite future lawyers, and indeed any reader, to re-imagine what the law is, what the law can be, and how to make the law more responsive to the needs of all people.
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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 | 0.033 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.032 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.009 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 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".