Troubling Diversity and Inclusion: Racialized Women’s Experiences in the Canadian Armed Forces
Bibliographic record
Abstract
This article centers on the lived experiences of racialized servicewomen in the Canadian Armed Forces (CAF). Drawing on qualitative interviews with racialized servicewomen, I problematize the function of contemporary diversity and inclusion initiatives within the CAF. Focusing on the intersection of race and gender in their lives provides a way to think through structural inequities within the Canadian military. By examining how these structures of power operate within the CAF, we are better situated to understand how current diversity and inclusion initiatives work to consolidate hegemonic power. Informed by feminist critical race theories and critical geography, I trace the experiences of racialized servicewomen to understand how they make sense of their inclusion and belonging and how they assess their everyday experiences in the context of diversity and inclusion strategies presented by the CAF. Their lived experiences reveal the importance of race and gender in their lives, and expose the limits of diversity and inclusion practices, particularly, in their inability to address deeper structural issues of white supremacy, heteronormativity, and patriarchy within the CAF. While concepts of diversity and inclusion are typically concerned with the inclusion of those on the margins, this research suggests that we must seriously interrogate the theoretical, practical, and political work of diversity and inclusion initiatives within a multicultural context. Troubling inclusion and diversity in the CAF demands we disrupt structures of dominance and reflect on how to re/conceptualize and re/integrate meaningful difference more substantially throughout institutional life in multicultural Canada.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.087 | 0.040 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| 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".