THE CANADIAN AFFIRMATIVE CONSENT MODEL AS A GUIDE FOR RETHINKING CURRENT U.S. STATUTORY TREATMENT OF VOLUNTARY INTOXICATION SEXUAL ASSAULTS
Bibliographic record
Abstract
Current U.S. statutory treatment of voluntary intoxication sexual assault is convoluted by the disconnect between definitions of consent, treatment of voluntary intoxication, and mens rea standards. Within this disconnect, the general public in many states are left largely unprotected when they become voluntarily intoxicated, such as through voluntary drinking. Being that voluntary intoxication is a highly socialized activity, and one that is prevalent across the nation, the need for reform that better protects against sexual assault is necessary. I argue that the Canadian affirmative consent model addresses these gaps in U.S. statutory law. The Canadian affirmative consent model provides a clear definition that recenters the conversation to consent, eliminates subjective evidence, sets a clear mens rea standard, provides protection across the intoxication spectrum and challenges dated sexual scripts. While changes to statutory law are most needed in politically conservative states, it may be more practical to first implement the standard in liberal states, where data can be collected to convince hesitant state legislators to pass the Canadian affirmative consent standard.
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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 | 0.062 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.020 | 0.033 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".