Advocating for minority Veterans in the United States: Principles for equitable public policy
Bibliographic record
Abstract
LAY SUMMARY Minority Veterans in the United States are often excluded, whether intentionally or not, from public policy initiatives, leading to approaches that attempt to account for, or include, minority Veterans after the policy process has begun rather than at the foundational stages. This leads to policies and programs that do not adequately serve or that may harm minority Veteran communities. Drawing on their work with the U.S. Senate and House Veterans’ Affairs Committees and the U.S. Department of Veterans Affairs, the authors outline four principles for equitable Veteran public policy to better support minority Veterans and their communities. These principles are grounded in intersectionality theory, a framework that starts from the recognition that everyone has multiple identities and that these identities relate to the inequalities one experiences personally and systemically. The authors hope these principles contribute to more equitable public policy analyses and practices to better serve minority Veterans and lessen instances of inequality or injustice.
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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 | 0.091 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.052 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".