Bibliographic record
Abstract
Sexual assault, along with the obscenity and criminal indecency provisions of the Criminal Code , is part of a family of offences directed at wrongful sexual objectification. Insofar as those offences all target pernicious forms of objectification, the ways in which each has been interpreted can reveal important things about the others. With that in mind, it is striking that the Supreme Court’s decisions in Butler and Labaye proceed on the basis that both section 163 and the offence of criminal indecency require proof that the conduct in question causes social harm. By emphasizing harm, the Court obscured the message that certain kinds of objectification are per se wrongful, whether or not we can point to any tangible harm. Perhaps more importantly, Butler and Labaye fail to provide the sort of sophisticated analysis of what makes conduct problematically objectifying in the first place. Even if these shortcomings produce no discernible effect on the way that courts decide particular cases before them, it undermines the educative function of the criminal law. This is especially problematic in the context of sexual assault, where the law must not only reflect social values, but take a leadership role in transforming them.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.047 | 0.017 |
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".