Bibliographic record
Abstract
Others in this volume have written compellingly about the rich tracings and occasionally tangled strands -that elegant web -of Blaine Baker's own history as a teacher and scholar of Canadian law.As a sometime student and colleague of Professor Baker, and as one of the many held in his thrall over the years, I would like to record a theme in his intellectual life that, I think, runs through many of the essays in this fine book and yet one that is not always as plainly feted as it should be.While his embrace of this province might have seemed, from a distance, to be a hesitant one, Blaine Baker was a Quebec law teacher for over thirty years.His identity as a Quebec academic figure rested on twin pillars: a deep attachment to McGill University's Faculty of Law which, it must be said, returned that favour; and a sense that some of his own values were reflected in a proper understanding of nineteenth-century Quebec legal history.All this grounded a place for Professor Baker in Quebec law teaching and scholarship, even if many of those working elsewhere in the province on law and history may have been unaware of the extent of his contributions.By way of modest thanks to him and to the editors for the privilege of writing this foreword, I would like to recall an underestimated but not inconsequential aspect of Blaine Baker's intellectual and social life in the law by pointing to these twin Quebec pillars and the measure of stability they brought to his work.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 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".