Define It! Addressing Rape Culture within 2SLGBTQ+ Communities Through Critical Consciousness Raising
Bibliographic record
Abstract
Sexual violence remains a global public health concern. Although evaluated rates of sexual victimization against LGBTQ+ individuals exist, prevention efforts primarily center cisgender and heterosexual experiences. The current study addressed this gap by evaluating the effectiveness of a sexual violence prevention program, Define it! adapted by and for LGBTQ+ communities. Data were collected at baseline and one-month follow-up from 36 LGBTQ+ students (intervention n = 22, control n = 14). Univariate ANCOVAs were run to investigate the impact of the intervention on critical consciousness, bystander willingness to intervene, and rape myth acceptance. Significant group differences were identified for bystander willingness categories of consciousness raising and sexual assault bystander behavior. Additional significant group differences were not identified. However, due to the small sample size, effect sizes were examined. Effects sizes ranged from large (η2 = .173, consciousness raising) to minimal (η2 = .001, sexual harassment bystander behavior). Results provide initial support for the utility of the adapted version of Define it! with LGBTQ+ communities, particularly in the areas of consciousness raising and sexual assault bystander behavior. These results also suggest the promise of prevention efforts centering LGBTQ+ individuals and their experiences and critical consciousness raising in ending sexual violence on campuses.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".