Dossier on the <i>Arthurs Report</i> on Law and Learning: A Reaction from University of Quebec in Montreal (UQAM)
Bibliographic record
Abstract
Two years ago, while preparing a one week seminar for law professors at the Université nationale du Rwanda in Butare, I had the occasion to revisit the Arthurs Report on Law and Learning. I remember that my reaction was: how did things change so significantly since the Report was published? That is why I was very interested to receive my new issue of the Canadian Journal of Law and Society that announced a complete dossier on the Report. After I read the first two articles by esteemed colleagues Roderick A. Macdonald and Constance Backhouse, I was shocked. How could there be such a gap between my perception and theirs? I was nevertheless relieved to see that Andrée Lajoie, in her “comments on the comments”, was as astonished as I was. I then decided to write my own comments in order to reflect a bit more the situation of the Civil Law faculties in Quebec and in particular the vision of the Université du Québec à Montréal (UQAM). I will limit my comments to three topics: the study of law at the undergraduate and graduate levels and the state of legal scholarship.
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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 | 0.022 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.037 | 0.015 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.030 | 0.027 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".