Contesting Criminal Law: Honouring The Work of Professor Don Stuart (Introduction to Special Issue)
Bibliographic record
Abstract
In recent years, few legal issues have provoked as much scholarly and public attention as sexual assault and, in particular, the sexual assault trial. This collection sheds critical light on this area, two papers directly and one more obliquely. Together, these papers also provide a riveting account of the role of trial courts and their relationship to prosecutors, appellate courts, and defendants and complainants. This volume brings together three leading criminal law scholars—Professors Janine Benedet, Steve Coughlan, and Lisa Dufraimont—whose work bears the influence of Professor Stuart just as his does theirs. Professor Don Stuart began his more than forty-year career teaching and writing about Criminal Law, Criminal Procedure, and Evidence at Queen’s in 1975, quickly establishing himself in Canada as a leading commentator on criminal law and evidence law, and eventually becoming, as Steve Coughlan donned him in this volume, “the Dean of Canadian criminal law academics.”
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".