Angelis: Inductive Reasoning, Post-Offence Conduct and Intimate Femicide
Bibliographic record
Abstract
Every week in Canada, a woman is killed by a current or former intimate partner. It is a serious systemic problem. To put it in perspective, the number of women killed by their intimate partners in 2011 was roughly comparable to the number of gang-related homicides. Many, if not most, of these cases involve intimate femicide, a term used to give effect to the gendered nature of the crime. R v. Angelis (2013) 99 CR (6th) 315 (Ont CA) appears to have been a case of intimate femicide. Unfortunately, the Court of Appeal did not construct the case in this fashion and, in ordering a new trial, failed to properly assess the relevance of the accused’s post-offense conduct on the critical issue of intent.R v. Angelis was a high profile case in Ottawa involving a husband who killed his wife during a struggle. The issue at trial was whether the killing was intentional or accidental. Angelis was convicted of second degree murder but his conviction was over-turned because the Court of Appeal held that the trial judge had erred in not leaving provocation as a defense. The Court of Appeal further held that the trial judge had erred in inviting the jury to draw an inference of intent from the accused's failure to perform CPR or call 911 after he discovered that his wife had stopped breathing during their struggle. It is the latter issue that is the subject of my comment.The piece examines what the common law has taught us about the indicators of intimate femicide and how that was relevant in engaging in inductive reasoning in this case to determine whether the accused’s failure to use his training as a nurse to try and revive his wife when she stopped breathing was evidence of his intent to kill her. The piece also explores the nature of inductive reasoning and its use in assessing the admissibility of after-the-fact conduct.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".