Relocating Officially Induced Error of Law: Fitting the Remedy to the Wrong
Bibliographic record
Abstract
The rule that ignorance or mistake of law is no excuse is one of the pillars of the criminal law. However, it can prove problematic in some cases including those where an individual commits an offence in reliance on incorrect official advice. This paper argues that the orthodox judicial approach to such claims fails to address the concern that the defendant is held responsible for what is essentially the state's own mistake. Rather than advocating a full defense of officially induced error of law, a more appropriate solution is for the court to exercise its inherent jurisdiction to prevent an abuse of process by staying the proceedings. This ‘procedural’ approach, which identifies the state's role in the commission of the offence, has recently been adopted by the Supreme Court of Canada, and has the potential to be applied in other common law jurisdictions including Australia, New Zealand and the United Kingdom.
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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 | 0.028 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.018 | 0.015 |
| 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".