Women Veterans: Examining identity through an intersectional lens
Bibliographic record
Abstract
LAY SUMMARY As the United States’ ethnic and racial demographics continue to reflect a nation of diversity, the U.S. Department of Defense (DoD) must also be mindful of diversity, equity, and inclusion (DEI). Currently, the DoD appears to be highlighting not only the ethical but also the strategic importance of diversity initiatives, but it must also strive to put theory into action to adequately lead and for Veterans to get proper medical and mental health care. Women, ethnic minorities, and lesbian, gay, bisexual, transgender, and queer/questioning individuals continue to enlist and face struggles to obtain adequate health care. Thus, this article discusses the need for intersectionality theory and critical race theory to be incorporated into ongoing discussions related to U.S. military and Veteran care. Although diverse backgrounds and experiences offer DoD the added benefit of diverse skill sets and innovation, it must also examine its own fighting force with a diverse lens, and in turn, the Department of Veterans Affairs should follow suit.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".