Law, Life, and the Teaching of Legal History
Bibliographic record
Abstract
As the leading legal historian of his generation in Canada and professor at McGill University for over three decades, Blaine Baker (1952–2018) was known for his unique personality, teaching style, intellectual cosmopolitanism, and deep commitment to the place of Canadian legal history in the curriculum of law faculties. Law, Life, and the Teaching of Legal History examines important themes in Canadian legal history through the prism of Baker’s career. Essays discuss Baker’s own research, his influence within McGill’s law faculty, his complex personality, and the relationship between the private and the public in the life of a university intellectual at the turn of the twenty-first century. Inspired by topics Baker took up in his own writing, contributors use Baker’s broad interests in legal culture to reflect on fundamental themes across Canadian legal history, including legal education, gender and race, technology, nation building and national identity, criminal law and marginalized populations, and constitutionalism. Law, Life, and the Teaching of Legal History offers a contemporary analysis of Canadian legal history and thoughtfully engages with what it means to honour one individual’s enduring legacy in the study of law.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".