Bibliographic record
Abstract
Justice Marc Rosenberg will be remembered as one of Canada’s greatest criminal law jurists by those fortunate enough to have worked with him, to have appeared before him, and now, by those who study and rely on his jurisprudence. He was a jurist who cared deeply about the fairness of the criminal justice system and he strived in every decision to arrive at a just result on the law and the facts. Many of Justice Rosenberg’s judgments reflect a concern for the constant struggle of triers of fact to accurately and fairly assess the credibility and reliability of evidence in determining historical events whether it be the testimony of the accused or central Crown witness. This piece explores three decisions from Justice Rosenberg which highlight the different ways in which stereotyping can distort the assessment of credibility and reliability in sexual assault cases: R v. Levert, R v. Rand and, R v. Stark. An important aspect of ensuring accuracy and fairness for Justice Rosenberg was the need to carefully regulate inductive reasoning: the engine that drives judicial reasoning and, ultimately, fact finding. The tools used for inductive reasoning include the decision maker’s or the law’s application of what it sees as common sense, logic and human experience. As an endeavour that explicitly relies on so-called common sense and generalizations about human experience, which shift with time, inductive reasoning can be highly subjective and can easily become a breeding ground for implicit bias, discriminatory stereotyping and unreliable decision-making.
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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 | 0.080 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".