Should I Stay or Should I Go? Employment Discrimination and Workplace Harassment against Transgender and Other Minority Employees in Canada’s Federal Public Service
Bibliographic record
Abstract
There is a growing literature interested in the workplace experiences of transgender individuals. The biggest limitation for researchers in this field continues to be the dearth of population-level data that captures information on gender identity and employment characteristics. Using the 2017 Public Service Employee Survey, this paper explores employment discrimination and workplace harassment against gender diverse (transgender, non-binary, genderqueer) and other minority employees working in Canada's federal public service. This study finds that gender diverse employees are between 2.2 and 2.5 times more likely to experience discrimination and workplace harassment than their cisgender male coworkers. Cisgender women, visible minorities, Indigenous, and those with disabilities are also more likely to report discrimination and workplace harassment. Cisgender women and gender diverse employees who occupy multiple minority statuses may experience an additive likelihood of discrimination and harassment. This study also finds that employee retention can be improved by providing more inclusive and tolerant workplaces.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".