Are Chapter 31 Military Veterans Returning to College More Likely to Choose Intrinsically-oriented Versus Extrinsically-oriented Majors?
Bibliographic record
Abstract
Helping military veterans successfully transition to civilian life is an important issue. Education can help with this transition. No prior studies were found on the general type of undergraduate major United States (US) Chapter 31 veterans enroll in. Chapter 31 provides tuition benefits to help entitled transitioning military veterans, with service-connected disabilities, go to college to obtain a degree. Self-determination theory (SDT) suggests two general categories of majors, intrinsic (I) versus extrinsic (E). Intrinsic motivation emphasizes doing a task for its inherent satisfaction, while extrinsic motivation targets doing the same task to achieve external rewards, such as compensation. Archival data was analyzed using small samples of undergraduate Chapter 31 military veterans in 2016, 2018 and 2020. Overall, the results supported the research question, i.e., Chapter 31 veterans will be more likely to choose intrinsically motivating versus extrinsically motivating college majors. Results, including limitations and suggestions for future research are discussed.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".