Still “Law” and Still “Learning”? Quel «droit» et quel «savoir?»
Bibliographic record
Abstract
The fact that we are celebrating the 20thanniversary of the Report of the SSHRC Consultative Group on Research and Education in Law and that the event is cast as a celebration of the Arthurs Report signals two key features of legal research and legal education in Canada today. To begin, it tells us that, at least in certain scholarly circles, theReporthas had an impact. That impact can be seen both in the mirror of the past, and in the lens of the present. Looking backwards, the early success of this Association and the founding of its review - theCanadian Journal of Law and Society– attest to the immediate galvanic effect of theReport; its continuing influence is manifest, notably, in the decision of the SSHRC last year to create a separate adjudication panel for law and society research. Between these salient bookends, one observes that theReporthas been called in aid of numerous projects, programmes and initiatives. Let me mention only two (with which I had some prior association) that took on a relatively permanent institutional form: the Law and Society (later Law and the Determinants of Social Order) Programme of the Canadian Institute for Advanced Research that flourished between 1986 and 1996; and the re-establishment in 1997 of a multi-disciplinary federal law reform agency – the Law Commission of Canada – that was charged with pursuing a law and society research mandate.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.031 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".