Competition, Cooperation or Cartel: A National\nLaw School Accreditation Process for Canada?
Bibliographic record
Abstract
Law schools in Canada are engaged in increased competition with one another and significant disparities in resources and reputations have developed. The author argues that this competitive context may be a threat to the maintenance in some schools of the broader mission of the law school to teach and produce contextual and critical perspectives on law. It is suggested that Canadian law schools should cooperate with each other and that various initiatives could be taken which would help all schools. Beyond cooperation on specific projects, the authorraises the question of whetherlawschools should set up theirown national accreditation scheme. He suggests various reasons why accreditation cannot be ignored any longer, then surveys the current ad hoc approach to accreditation by the profession in Canada, and finally provides an overview of the accreditation of law schools in the United States, focussing on the controversy of whether accreditation is a form of cartelization. The author is ambivalent about accreditation, but believes that the issue must be examined and debated as an option in the face of the disturbing trends engendered by increasing competition.
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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.039 | 0.013 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".