Beyond "Restoration of Honor": Compensating Veterans for the Psychological Injuries of the Gay and Transgender Bans
Bibliographic record
Abstract
This Article is titled “Beyond Restoration of Honor” specifically to introduce the policy priority of ensuring that all Sexual and Gender Identity Minority (SGIM) veterans who were harmed by...discriminatory policies [like Don't Ask, Don't Tell] can obtain and use Veterans Affairs (VA) disability benefits for injuries resulting from discrimination while in the military. While this Article highlights the value of codifying a series of specific SGIM stressor markers for PTSD in the VA’s regulations concerning personal assault and creating presumptions of service-connection for specific military experiences, existing laws and regulations permit service-connection for these injuries without further regulatory changes. In recognition of the policy concerns facing this large, under-served group of military veterans, this Article adopts a three-step approach. Part I briefly explores the relationship between SGIM status and adverse mental health outcomes among U.S. veterans. This Part pays particular attention to the characteristics of the anti-gay bans that have theoretically caused mental health injuries. Part III then examines the existing VA disability framework for compensating mental health injuries. This Part identifies VA disability compensation as the appropriate vehicle to address the unmet needs of impacted SGIM veterans. Part III describes the research methodology and results of a study that identified and analyzed VA disability appeals in which veterans claimed that SGIM orientation discrimination caused their mental health condition. Through natural language processing (NLP) strategies and machine learning (ML) algorithms, the study identified 118 Board of Veterans’ Appeals cases out of 123,011 decisions addressing service-connection for mental health disorders. This Part presents the results of statistical analysis of the relationships between case outcomes and case characteristics. It specifies the types of mental health conditions most often claimed and awarded in SGIM discrimination cases, the demographic background of the veterans who appealed, and other factors related to the success and failure of these claims. As an aid to practitioners, this Part introduces an Online Supplement containing a digest of summarized cases, indexed by different facts which may resemble the background of a future veteran’s claim. The last Part concludes with recommendations to ensure that those veterans who have been impacted by the military’s discriminatory policies are able to address longstanding needs and overcome persistent stigma surrounding requests for assistance. This Part discusses the benefits of developing a presumption related to SGIM discrimination in the regulations related to traumatic stressors. It also explores Canada’s recent experience developing a comprehensive governmental approach to veterans who experienced the Gay Purge and is a noteworthy example of success in the restoration of honor. It further draws salient lessons from cases litigated under the present adjudication framework. In sum, the Parts below offer a comprehensive roadmap for immediate action—well beyond simply the restoration of honor. This abstract has been adapted from the author's introduction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".