Bibliographic record
Abstract
These brief remarks offer a few reflections on Chief Justice McLachlin’s contributions to the Supreme Court of Canada’s jurisprudence on the division of powers, based on cases where she authored or co-authored reasons for judgment. It is obviously daunting to try to comment on the jurisprudence of the longest-serving Chief Justice in Canadian history. But the task certainly repays the effort and only deepens one’s admiration for her many important contributions to Canadian law. In that spirit, these notes provide a few comments on Chief Justice McLachlin’s judicial philosophy and her contributions to legal federalism and legal education. I will argue that Chief Justice McLachlin’s federalism jurisprudence fairly reflects her self-described judicial philosophy as being scrupulously non-partisan and impartial. I will further suggest that her contributions to the doctrines of legal federalism, as seen in her interjurisdictional immunity rulings by way of example, brought greater stability, certainty, and clarity to the law. I will close by suggesting that the rigour and lucidity of her judicial writing have contributed significantly to legal education in Canada.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".