Teaching Canadian Labour and Employment Law in the Globalized New Economy: Ruminations of an Aging Neophyte
Bibliographic record
Abstract
These remarks are subtitled "ruminations of an aging neophyte," in recognition of my somewhat odd status as a labour law teacher. On the one hand, I've been at Dalhousie Law School for more than 30 years, teaching full time and conducting research in the areas of criminal law, criminal procedure, evidence, comparative law and, latterly, restorative justice. On the other hand, while I studied labour law with Innis Christie at Dalhousie using the first edition of the national labour and employment casebook in the early 1970s, I began teaching the subject only four years ago, and feel very much a neophyte. This does not mean that I have no experience in the field of labour and employment law. Having acted part-time as a labour arbitrator in private-and public-sector rights disputes under collective agreements since the early 1980s, and had many years' experience serving on labour relations and employment law tribunals, I have a reasonably secure sense of how our major institutions in that field operate. I also keep up connections with members of the labour relations community across Canada, who represent its disparate and often polarized cultures. But when I recently came to teach labour law, I was forced to come to grips with the "big picture," which can in many ways be avoided by someone "in the trenches."
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.040 | 0.049 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".