Trauma-informed Consent Education: Understanding the Grey Area of Consent Through the Experiences of Youth Trauma Survivors
Bibliographic record
Abstract
Sexual consent education has emerged in recent years as the most popular method of preventing gender-based violence. Yet, the concept of consent used in much contemporary programming problematically oversimplifies sexual exploration and the power dynamics it is imbued with by asserting that consent is as simple as “Yes” or “No.” The messiness of sexual negotiation or the ‘grey areas’ of consent that youth may experience are left unaddressed. By examining the experiences of youth trauma survivors through a trauma-informed lens, the limits to binary consent education become clear. I draw on empirical data from nine open-ended interviews with Canadian youth trauma survivors to demonstrate how a trauma-informed lens may be implemented in consent education. I argue that educators should include understandings of consent which falls outside the Yes/No binary in order to adequately address youth survivors’ vulnerability to sexual (re)victimization. I examine how three of the psychosocial impacts of trauma, dissociation, hypersexuality, and struggles with acquiescence, refuse the binaristic model of consent and should be considered for trauma-informed consent education. While education alone cannot end rape culture, addressing the grey area of consent in consent education may help reduce preventable harm for survivors, as well as youth more broadly.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.058 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".