Bibliographic record
Abstract
In my contribution to this discussion or theArthurs Reporttwenty years later, I want to talk briefly about the teaching aspect of legal education. I want to be emphatic in my focus on teaching because I fear that teaching is increasingly diminishing as an object of our attention and as a subject of our scholarly work. I fear that, for many of us, teaching is becoming a less and less significant part of our discussions, our hirings, our preoccupations, our energy … To me, this is both short-sighted and sad, particularly for those of us who are committed to a law and society approach to legal education. If we are committed to social change, then education, in the form of teaching and learning, is critically important – that is, what we teach and how we teach, and what we model as teachers and thinkers are important instigators and promoters of change. I make this assertion of a lack of serious interest in law teaching despite the fact that there have been a number of Canadian forums on legal education recently. However, admissions and administrative matters largely overshadowed teaching as issues of primary concern and discussion in the two forums that I attended. I do not think these forums generated the kind of on-the-ground, in-our-work-places re-energization and rethinking of legal education and specifically about teaching that was perhaps hoped for.
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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.035 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".