Bibliographic record
Abstract
Consent is an extremely important principle within the law—so important it is defined twice within the Criminal Code: first in s. 265, the assault provision which also governs the law of sexual assault, and again in s. 273.1, where the Code provides additional definition specifically within the context of sexual assault. The limits of consent have been further defined through case law, especially in R v Ewanchuk. Canadians would be surprised to discover that Ewanchuk determined it is illegal to initiate sexual activity via sexual touching, or to kiss a sleeping spouse. To keep people safe from inappropriate sexual touching, we have outlawed activities most intimate partners would not object to: this creates a dilemma about when protection exceeds its necessity and becomes inappropriate control of sexual autonomy. It is this dilemma this article addresses. Ultimately, this article argues that Canada’s sexual assault laws must change for two reasons: first, by criminalizing behaviour that is not morally wrong, the criminal law is overbroad and doesn’t fulfil its expressive function, and; while enacted with the noble goal of protecting the sexual autonomy of women, our consent laws serve to restrict the sexual autonomy of women in ways that are objectionable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.026 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".