Bibliographic record
Abstract
Over the past two decades, the Supreme Court of Canada has developed an overarching account of causation rooted in the need to prevent the conviction of the morally innocent. Despite these valuable contributions, there are certain limitations to the way causation is currently conceptualized in Canadian criminal law. This article aims to address those limitations and offer a plausible alternative account of causation and its underlying rationale. It advances three core arguments. First, it explains why judges should employ one uniform formulation of the factual causation standard: significant contributing cause. Second, it offers a new account of legal causation that distinguishes foreseeability as part of the actus reus from foreseeability inherent to mens rea. In doing so, it sets out why legal causation is primarily concerned with fairly ascribing ambits of risk to individuals. Third, it refutes the Supreme Court of Canada’s underlying justification for the causation requirement. Contrary to the Court’s invocation of the importance of moral innocence, this article demonstrates that causation principles actually tend to concede the accused’s moral fault while still providing reasons for withholding blame for a given consequence. This reveals that causation’s underlying rationale is more closely related to concerns about fair attribution rather than moral innocence. Ultimately, this article reframes causation to better answer one of the most basic questions in the criminal law: Why am I being blamed for this?
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.044 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".