Necessity and Death: Lessons from Latimer and the Case of the Conjoined Twins
Bibliographic record
Abstract
The availability of the defence of necessity in cases of homicide is a complex issue in both Canadian and British jurisprudence. This article examines the divergent judicial and academic views and argues that, while necessity may be available for certain kinds of homicide, it should be rejected as a legitimate defence to intentional killings. The author looks closely at two recent cases in which the question arose as to whether or not killing a human being is ever justifiable or excusable on the basis of necessity: the Canadian case of R. v. Latimer and the British case of Re A (Children). The author argues that the approach of the Latimer court is preferable, advancing this position from a number of angles. Underlying rationales for the defence of necessity in Anglo-Canadian jurisprudence are examined, as well as the conceptually similar defence of duress, both at common law and in s. 17 of the Criminal Code. Both of these points are reinforced and analyzed via a discussion of the sanctity-of-life principle in Canadian criminal law. The article makes clear the essential nature of the issues raised in both Latimer and Re A (Children), as they engage fundamental questions of value for our society.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".