Bibliographic record
Abstract
There is resounding consensus that diversity in legal education is a priority. Yet, North American law schools continue to be criticized for failing to reflect the diversity of the society that they are training lawyers to serve. This article is a project of conceptual reorientation against a backdrop of critical scholarship and empirical evidence. Parts I and II examine the past twenty years of diversity promotion in legal education, concluding that, while several advances have been made, especially in increasing numerical representation of diverse groups in law schools, the promise of meaningful diversity remains unfulfilled. Part III suggests that reforms in legal education, though well-intentioned, have continued to focus on the production of a model of professional identity that is out of reach and out of touch for many minority students. In Parts IV and V, the author outlines a program for transforming the norm of lawyering taught in law school. Grounded in a normative framework of access to justice and equality, the author argues that experiential/clinical learning practices offer a useful method to achieve a more engaged pedagogical commitment to diversity in legal education.
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.
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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.034 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".