Canadian Constitutional Law: Presentation to the Annual Conference of International Association of Law Libraries
Bibliographic record
Abstract
Abstract When I was asked to give this talk it occurred to me that it might be interesting to think aloud about some of the changes in constitutional law—and in writing about constitutional law—that have occurred since I came to Canada. I am a New Zealander by birth, but I was teaching at the Faculty of Law of Monash University in Melbourne, Australia, when I came to the Osgoode Hall Law School on a one-year visit in the summer of 1970. During that visiting year, the faculty decided to offer me a permanent appointment. This was done over the objection of one of my colleagues, R. J. Gray, who claimed that my lectures would require simultaneous translation, and that I would not meet the height requirements for Canadian citizenship. Anyway I was persuaded to stay (and three years later I became a Canadian citizen).
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.024 | 0.004 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.084 | 0.007 |
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".