PRESIDENTIAL SYMPOSIUM: LONG-TERM OUTCOMES OF MILITARY SERVICE ON AGING: INTERNATIONAL PERSPECTIVES
Bibliographic record
Abstract
Many studies of aging have been conducted on samples that include WWII and Korea-era veterans; thus, military service a “hidden variable” in aging research. The impact of service on later life development and aging is poorly understood, yet its effects are often broad and long-ranging, and can alter lives in positive as well as negative ways. This symposium considers the long-term effects of military service on health and well-being of veterans who served in the armed forces. Presenters are from several nations (Israel, Canada, Vietnam, Korea, and United States), and use a lifespan/life course perspective to examine the impact of service on later-life outcomes in veterans from various conflicts. Solomon and colleagues examine long-term effects of being a POW among Israeli veterans. Pedlar examines changes in Canadian veterans after service, and compares them to non-veterans. Korinek examines older Vietnamese war survivors, and the impact of wartime exposure on intergenerational relations. Kang and colleagues provide a lifespan examination of the long-term effects of combat exposure in Korean Vietnam War Veterans, while Lee and colleagues examine possible pathways for positive outcomes of combat exposure among US veterans from WWII and Korea. Dr. Settersten will be our discussant, focusing on common themes among studies, and the implications of military service for changing lives across countries.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".