“Disassociated Sex and the Trouble With ‘Healthy’ Consent Models for Sexual Violence Prevention”
Bibliographic record
Abstract
In the wake of #MeToo, measures to address sexual violence have been demanded by survivors and allies, and consent education has come to be considered the foremost solution to addressing an epidemic of sexual violence (Schneider & Hirsch 2018). This shift isthe result of decades of transnational feminist scholarship emphasizing a need for clear practices and laws underscoring consent in order to end sexual violence (West 2009; Heise et al. 1995). Yet, consent education programming often relies upon an overly-simplistic, binary version of consent that sets an impossible standard for many youth who are dealing with the psychosocial impacts of trauma, which this presentation will demonstrate.
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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 | 0.048 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.116 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".