Hearing their voices and counting them in: The place of Canadian LGBTQ police officers in police culture
Bibliographic record
Abstract
The growing presence of LGBTQ police officers and civilian personnel within police organizations, their presence at LGBTQ community events, increased recruitment efforts, and the emergence of LGBTQ advocacy groups within polic-ing invites research into the lived experiences of these police service members. My 2014 study of 21 LGBTQ sworn police officers in Ontario revealed that most officers believe their status and relationships in their workplaces are more positive today compared to other eras. However, it also found that they believe that police culture fundamentally retains a hyper-masculine and heterosexual orientation. A subsequent study of the intersectionality of gender and sexual orientation for gay female sworn police officers found that being “female” and being “gay” exposes LGBTQ female police officers to challenges regarding both their gender and their sexual orientation—specifically workplace harassment and having to conform to masculine “norms”. However, the research also suggests that these and other challenges in a police environ-ment based on sexual orientation are not as overt as those based on gender alone. Understanding such subtle differences is vital to creating inclusive and supportive work environments in which LGBTQ members can thrive and contribute as their authentic selves and find legitimacy and respect as police professionals.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.060 | 0.021 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".