Harassment and discrimination experiences of Indigenous Peoples in the Canadian Armed Forces
Bibliographic record
Abstract
Introduction: The experiences of Indigenous Peoples in the Canadian Armed Forces (CAF) have largely been unexplored. Research in civilian settings has indicated that Indigenous Peoples face interpersonal mistreatment at higher rates than their non-Indigenous peers. Thus, the authors suggest that Indigenous CAF members may face similar obstacles during their service. Methods: Using data from the CAF Harassment and Discrimination Survey (N = 4,715), the authors investigated the experiences of Regular Force members who self-identified as Indigenous (N = 487). Nine multinomial logistic regressions were conducted to determine the patterns of harassment (personal harassment, abuse of authority) and discrimination experienced by Indigenous members. Results: Relative to non-Indigenous members, Indigenous members had an increased likelihood of experiencing both personal harassment and abuse of authority; however, Indigeneity explained less than 1% of the variance. After controlling for gender, visible minority identity, and rank group, Indigenous members showed an increased likelihood of experiencing personal harassment, abuse of authority, and discrimination relative to non-Indigenous members. Controlling for other known factors also increased the variance the model accounted for to 4% to 7%. Discussion: Overall, the results suggest that Indigenous members are at increased risk for interpersonal mistreatment during their service in the CAF. Future studies should incorporate intersectionality into their research design to further investigate the experiences of Indigenous members.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".