Bibliographic record
Abstract
Harry Arthurs' Law & Learning report of 1983 documented the low productivity of academic legal scholars in Canadian law schools. It recommended creating distinct streams for the academic study of law and professional legal education. This paper discusses developments to the legal scholarship landscape twenty years after the report. Positive changes include increased research grants available from SSHRC and a wealth of inspired interdisciplinary and critical legal scholarship. Some issues mentioned in the report remain problematic. Law professors still tend to have relatively little academic research training. Canadian educators still know little about how law school can prepare students for legal practice. Law schools face new trends and pressures not conceived of in Harry Arthurs' report. More women and racialized Canadians are entering the legal profession. Increased American influence and the privatization of universities have caused pressure to establish a hierarchy of Canadian law schools, and for schools to strategically specialize in niche areas of law. The article recommends diversification of law school faculties and student bodies, diversification of research, and diversification of curricula to accommodate more academic and social justice-focused course work.
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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.018 | 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".