Queering Legal Education: A Project of Theoretical Discovery
Bibliographic record
Abstract
The article has two parts. Part II discusses the materials we reviewed to inform the development of a queer legal pedagogy. In particular, it examines the categories of queer legal scholarship and highlights the contributions of other outsider scholars to legal education debates. Early in our research, we found limited material on queer legal pedagogy, and we discovered nothing that posited a theoretical approach. We did, however, find rich resources written by other outsiders to law from which some design principles for queer legal pedagogy might be drawn. We should note at the outset that our goal in this Part of the Article is not to review queer scholarship or outsider approaches to legal pedagogy exhaustively, nor is it to provide a literature review. Instead, our purpose is simply to acknowledge the contributions of these scholars to our understanding of what queer legal pedagogy might be by highlighting some of the fundamental insights in that scholarship. Part III of the Article proposes eight principles of queer legal pedagogy. Although we conclude that it is premature to suggest an overarching theoretical approach to this pedagogy, we present these eight principles to begin a discussion of what a queer legal pedagogy might look like. We also attempt to apply these principles to the law school curriculum, making some preliminary remarks about how the curriculum might be queered. Finally, the Article concludes with some tentative thoughts about the transformative power of queer legal pedagogy.
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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 | 0.038 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.009 | 0.066 |
| Scholarly communication | 0.017 | 0.030 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| 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".