Introduction to Law, Authority & History: A Tribute to Douglas Hay
Bibliographic record
Abstract
On 5 and 6 May 2016, Osgoode Hall Law School and the York University History Department sponsored a symposium entitled “Law/Authority/History: A Tribute to Douglas Hay” to mark the recent retirement of Professor Douglas Hay. The call for papers circulated to legal historians in Canada and elsewhere, and a particular attempt was made to contact Professor Hay’s former graduate students. Twenty papers were presented at the symposium, of which eight appear in this issue of the Osgoode Hall Law Journal. It seemed self-evidently appropriate to us to recognize this milestone in Douglas Hay’s career, in his home town, and at the University where he has served with such distinction as a faculty member for some thirty-five years. Douglas Hay received his Master of Arts degree in history from the University of Toronto in 1969 and then pursued his doctorate at the University of Warwick under the iconic historian of law and society E.P. Thompson. After seven years as a faculty member in the history department at Memorial University of Newfoundland, Douglas Hay spent a year as a visiting professor of Canadian Studies at Yale before being hired at York in 1981. Cross-appointed in law and history, Hay’s appointment at Osgoode was unusual, as Canadian law faculties did not at the time normally appoint candidates without a law degree. After a two-year stint back at Warwick from 1982-1984, Hay returned to Osgoode permanently, where he has been a mainstay of legal history teaching and research.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.037 | 0.015 |
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".