Sexual Consent as Voluntary Agreement: Tales of “Seduction” or Questions of Law?
Bibliographic record
Abstract
This article proposes a rigorous method to map the law on to the facts in the legal analysis of sexual consent using a series of mandatory questions of law designed to eliminate the legal errors often made by decision makers who routinely rely on personal beliefs about and attitudes toward “normal sexual behavior” in screening and deciding cases. In Canada, sexual consent is affirmative consent, the communication by words or conduct of “voluntary agreement” to a specific sexual activity, with a specific person. As in many jurisdictions, however, the sexual assault laws are often not enforced. Reporting is lowest and non-enforcement highest in cases involving the most common type of assailants, those who are not strangers but instead persons the complainant knows, often quite well—acquaintances, supervisors or coworkers, and family members. Reliance on popular narratives about “seduction” and “stranger-danger” leads complainants, police, prosecutors, lawyers, and trial judges to truncate legal analysis of the facts and leap to erroneous conclusions about consent. Wrongful convictions and perverse acquittals, questionable plea bargains and ill-considered decisions not to charge, result. This proposal is designed to curtail the impact of prejudgments, assumptions, and biases in legal reasoning about voluntariness and affirmative agreement and to produce decisions that are legally sound, based on the application of the rule of law to the material facts. Law has long had better tools than the age-old and popular tales of “ravishment” and “seduction.” Those tools can and should be used.
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 | 0.067 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.181 |
| Scholarly communication | 0.018 | 0.048 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".