Impact of a Cohort Model on the American Veteran Transition to College
Bibliographic record
Abstract
Veteran student populations are on the rise across the U.S. due to benefits from the revised Post 9/11 Government Issue (G.I.) Bill that guarantees financial assistance for housing and education for exiting service members. Institutions seeking integration practices for this student population may fail to acknowledge the multiple identities that veterans bring to the campus community and, thus, do not provide proper social support for this unique student population. The purpose of this case study was to examine how one veteran student cohort program provides support for veterans transitioning to their new student identity, and with retention and degree completion at one large California State University. This instrumental case study was conducted utilizing one-on-one in-depth interviews and archived document review to examine how effective the Veteran Education Program assists veteran students at Fresno State. The findings of this research show how students that partake in a veteran cohort program transition better into the broader campus community. As veterans begin transition into a new college student identity, pre-existing identities compete in the reprioritizing process.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".