The Prejudicial Effects of 'Reasonable Steps' in Analysis of Mens Rea and Sexual Consent: Two Solutions
Bibliographic record
Abstract
This article examines the operation of “reasonable steps” as a statutory standard for analysis of the availability of the defence of belief in consent in sexual assault cases and concludes that application of section 273.2(b) of the Criminal Code, as presently worded, often undermines the legal validity and correctness of decisions about whether the accused acted with mens rea, a guilty, blameworthy state of mind. When the conduct of an accused who is alleged to have made a mistake about whether a complainant communicated consent is assessed by the hybrid subjective-objective reasonableness standard prescribed by s. 273.2, many decision-makers rely on extra-legal criteria and assumptions grounded on their personal experience and opinion about what is reasonable. In the midst of debate over what the accused knew and what steps were “reasonable” given what the accused knew, the legal definition of consent in section 273.1 is easily over-looked and decision-makers focus on facts that are legally irrelevant and prejudice rational deliberation. That is precisely what we see here; the result is often failure to enforce the law. The author proposes: (a) that section 273.2 be amended to reflect the significant developments achieved in sexual consent jurisprudence since enactment of the provision in 1992; and (b) that, in the interim, the judiciary act with resolve to make full and proper use of the statutory and common law tools that are presently available to determine whether the accused acted with mens rea in relation to the absence of sexual consent.
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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 | 0.093 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.078 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.012 | 0.024 |
| 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".