In Defence of the Due Diligence Defence: A Look at Strict Liability as Sault Ste. Marie Turns Forty in the Age of Administrative Monetary Penalties
Bibliographic record
Abstract
The author looks back at the Supreme Court of Canada’s creation of a presumption that public welfare offences are strict liability offences affording defendants the opportunity to make out a due diligence defence. It is argued that the availability of this defence is receding in the face of a resurgence of absolute liability by means of administrative monetary penalties, which are increasingly being used by regulators to enforce compliance with regulatory requirements. While there is some support for the availability of the due diligence defence in the face of a potential administrative monetary penalty, the courts remain divided and numerous statutes expressly exclude it. It is argued that this ignores the important function that strict liability offences have served in encouraging corporate social responsibility through the development of compliance programs with the dual purpose of preventing harm and being able to demonstrate the taking of all reasonable care.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.053 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.032 | 0.032 |
| Insufficient payload (model declined to judge) | 0.004 | 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".