"They Just Looked At Me Like I Didn't Matter"- The Experiences Of Assaulted LGBTQ+ Women With The Police
Bibliographic record
Abstract
Research reveals that LGBTQ+ women are subjected to high rates of sexual and gender-based violence (SGBV). It is widely known that most victim-survivors of SGBV do not report it to the police and when they do, they usually have negative experiences, often due to the influence of myths and stereotypes on police officers’ attitudes and conduct. LGBTQ+ communities are known to be subjected to a multitude of myths and stereotypes that perpetuate violence against them, particularly in the case of highly marginalized subgroups (e.g., trans women, bisexual women). Little attention has been paid, particularly in the Canadian context, to the role of myths and stereotypes in LGBTQ+ women’s decision-making processes about whether to report SGBV to the police and experiences when it is reported. This thesis aims to address this gap and work towards practical solutions, through qualitative interviews with the frontline experts who support LGBTQ+ survivors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".