What’s Good for the Goose Is Good for the Gander: Considering the Merits of a Presumption of Organizational Capacity in Canadian Criminal Law
Bibliographic record
Abstract
A disconnect has emerged between the promise of the 2004 Westray reforms, which stressed the importance of holding organizations criminally responsible separately from individuals, and the state of current enforcement, which continues to be dictated by the availability of a single culpable individual on whom prosecutors can readily pin the elements of the offence. Is there a way to encourage enforcement more consistent with the spirit of the reforms? I suggest that one way of doing so is to create a presumption of organizational capacity, analogous to s. 16 Cr.C. Anchored to the idea that organizations are designed to do what is necessary to conduct their affairs, the presumption would simplify proof of what seems obvious – that the source of wrongdoing that occurs in the course of the pursuit of organizational goals is, absent proof to the contrary, best analyzed as a collective rather than individual phenomenon.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.032 | 0.045 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".