Teaching with Feminist Judgments: A Global Conversation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it