Bibliographic record
Abstract
Jerome Frank may have suggested the term Clinical Legal Education (CLE) first when he asked “Why Not a Clinical Lawyer School?”2; but, it was not until the New York City based Council on Legal Education for Professional Responsibility (CLEPR), funded by the Ford Foundation, took the pre-eminently active role in promoting and supporting law school-based experimentation in the 1970s and 1980s that CLE truly had an opportunity to develop. Over the past thirty plus years CLE has become more and more central to legal education, especially in the United States; innovations elsewhere have been fewer, more modest, and slower to develop, but of significance to the shifting culture of law learning, wherever they have taken place. The inception of the Journal3 marks an important milestone in the continuing development of CLE; for with this volume, we formally recognise that CLE is a vitally important and diverse phenomenon with a global reach.
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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.012 | 0.020 |
| Insufficient payload (model declined to judge) | 0.032 | 0.011 |
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".