Understanding and addressing sexual- and gender-based harassment and discrimination among Royal Canadian Mounted Police
Bibliographic record
Abstract
The current study sought to examine experiences of gender- and sexual-based harassment and discrimination within the Royal Canadian Mounted Police (RCMP), and what changes, if any, RCMP think are needed to address these issues within the organization. A representative sample of serving RCMP (n = 870; 72.6% men; 83.6% White) answered an open-ended survey question examining participants’ perspectives of how the RCMP can work to eliminate harassment and discrimination in RCMP workplaces. Responses were analyzed inductively, using an open-coding approach. Thematic analysis was used to identify themes and frequency analysis was performed to assess for recurrent concrete recommendations. Three themes were identified: Walking the Talk: Broad Accountability; Out with the Old, in with the New: Largescale Cultural Change; and Alternative Perspective: An Exaggerated or Non-Existent Problem. The most common concrete recommendations were to enforce actual consequences (n = 172), to have more effective leadership (n = 138), and to change the promotion process (n = 116). The current results indicate that many RCMP conceptualize gender- and sexual-based harassment and discrimination within the RCMP as a two-tiered problem, involving the actions of individual perpetrators and insufficient organizational responses to those actions. Results further evidence individual and structural hegemonically masculine elements within the RCMP organizational culture. Participant suggestions provide possible actionable solutions.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".