Bibliographic record
Abstract
We can no longer continue to teach consent education and sexual violence prevention in the same way. In 2019, the Ontario Student Voices Survey made it clear that the vast majority of postsecondary students could identify what consent should be, and yet the survey also made it clear that rates of sexual violence continued to be incredibly high. Knowing the definition of consent is thus apparently not the primary issue in the prevention of sexual assault. Rather, we should enter into dialogue with students to unearth and challenge the beliefs and behaviors that continue to foment rape culture. Undressing Consent was launched in January 2022, piloting an innovative and unprecedented scale of consent education. The program is unique because it does not teach what consent is technically, but rather focuses on challenging and unpacking the values and behaviors that get in the way of translating the rate of consent definition knowledge into a decrease in the rates—currently one in three—of students experiencing gender-based and sexual violence. In this chapter, experiences and lessons from the program developer and one facilitator are shared, in addition to the programming details, how they went beyond consent as knowledge, and what was achieved.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".