Australian Women Veterans’ Experiences of Gendered Disempowerment and Abuse Within Military Service and Transition
Bibliographic record
Abstract
Disempowering experiences of military service and transition for women veterans exist within an established, dominant, masculinised culture, in which their presence is highly visible, challenged, and often subject to institutional prejudice. Sexual abuse of women in the military, in particular, is a persistent finding in contemporary international research and national inquiries into military culture in countries such as Australia, the United Kingdom (UK), the United States (US), and Canada. This study sought to understand military service, transition to civilian life, and post-military experiences of Australian women veterans, specifically their experiences of discrimination, military sexual harassment and assault, and consequent military sexual trauma (MST). In-depth qualitative interviews were undertaken with 22 Australian women veterans that examined how women veterans manage their identity as women in the military. Issues included gender-based challenges in conforming to a masculinised culture, experiences of misogyny, sexual harassment and assault, systemic failures to recognize women's specific health needs, and experiences of separation from the military and transition, including help-seeking and engagement with services to address their experiences of MST. Women veterans' adverse experiences largely stemmed from an entrenched masculinised military culture, in which military sexual assault was enabled, ignored, and condoned. Military and veteran support services have been slow to recognize, acknowledge, and address this significant issue.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".