The 'Grey Area' of Consent: Understanding the Psychosocial Impacts of Trauma on Sexual Consent
Bibliographic record
Abstract
In recent years, sexual consent education has proliferated and arguably become the most popular way of preventing gender-based violence (GBV), particularly on post-secondary campuses. However, mainstream consent programming applies a reductive conception of consent and lacks a trauma-informed lens. The concept of consent that much programming relies upon is binary (‘Yes’/‘No’) and obscures ongoing yet preventable harm in the ‘grey area’ of consent. This disproportionately impacts youth trauma survivors as they are more than twice as likely to be sexually (re)victimized compared to their peers without trauma histories; moreover, the psychosocial impacts of trauma, such as substance dependence, dissociation, or hypersexuality, refuse a binary model of consent. To explore these phenomena, I interviewed 8 diverse undergraduate students at the University of Toronto who self-identify as trauma survivors. Using intersectional feminist theory and critical trauma studies as a theoretical framework, the project provides an evidentiary and conceptual basis for rethinking consent education with a trauma-informed approach. I found that trauma survivors were able to shed light on more complex conceptualizations of consent because they could not subscribe to, or fit their experiences into, a neatly packaged version of ‘healthy,’ consensual sexual experience. Participants in the study said that they were previously taught that consent is the key to positive, healthy sexual experiences—as if bad or regrettable sex could be abolished through consent—and this message did not reflect their lived experiences. The awkwardness, ambiguity, or ambivalence that may arise out of subjective sexual experiences, and not just those of survivors of trauma, remains an understudied, critical gap in consent education. This study contributes to the evidence that we must attend to the grey areas of sex and harm in order to understand more nuanced dynamics for negotiating mutually pleasurable, ethical sex, as well as the more insidious and uncontested manifestations of GBV. By recognizing the limits of contemporary consent discourses, we can expand visions of sexual justice and prioritize talk of mutuality, interdependence, and compassion in order to foster a greater sexual ethic of care.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.070 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".