No Women’s Land: Australian Women Veterans’ Experiences of the Culture of Military Service and Transition
Bibliographic record
Abstract
Women's experiences of military service and transition occur within a highly dominant masculinized culture. The vast majority of research on military veterans reflects men's experiences and needs. Women veterans' experiences, and therefore their transition support needs, are largely invisible. This study sought to understand the role and impact of gender in the context of the dominant masculinized culture on women veterans' experiences of military service and transition to civilian life. In-depth qualitative interviews with 22 Australian women veterans elicited four themes: (1) Fitting in a managing identity with the military; (2) Gender-based challenges in conforming to a masculinized culture-proving worthiness, assimilation, and survival strategies within that culture; (3) Women are valued less than men-consequences for women veterans, including misogyny, sexual harassment and assault, and system failures to recognize women's specific health needs and role as mothers; and (4) Separation and transition: being invisible as a woman veteran in the civilian world. Gendered military experiences can have long-term negative impacts on women veterans' mental and physical health, relationships, and identity due to a pervasive masculinized culture in which they remain largely invisible. This can create significant gender-based barriers to services and support for women veterans during their service, and it can also impede their transition support needs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".