Responsibility and Intervening Acts: What “Maybin” an Overbroad Approach to Causation
Bibliographic record
Abstract
Oftentimes, a criminal action resulting in the victim’s death is clearly attributable to the accused. In many cases, we can easily say that the accused “caused” the victim’s death. Causation, however, becomes particularly complicated when some type of intervening act occurs between the accused’s initial conduct and the victim’s death, creating speculation about whether it is fair to morally blame the accused for the ensuing result. The Supreme Court of Canada’s relatively recent decision R v Maybin marks a significant attempt to clarify notions related to causation in the criminal law. Although the Court refused to alter conventional principles related to the law of causation, or create a new test to verify when it has been established, it provided two analytical tools which can be used in order to see when it is fair to morally blame the accused for the victim’s death despite an intervening act’s occurrence. As will be seen, although these analytical tools of “reasonable foreseeability” and “independent acts” serve to simplify the law of causation, there are important problems with how each tool has been conceptualized. This article highlights these important shortfalls, and ultimately, questions to what extent these developments in the law of causation affect current conceptions of mens rea.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.055 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".