An Analysis of the Relationship of Military Affiliation to Demographics, New Sailor Survey Responses, and Boot Camp Success
Bibliographic record
Abstract
This study examines the relationship of military affiliation to demographics, New Sailor Survey responses administered during fiscal year 2007, and graduation from boot camp. A recruit was categorized as having military affiliation if parents or siblings of the recruit had served or were serving in the military. Recruits' military affiliation showed no significant relationship with AFQT scores, age, bonus amounts, college level, graduation rate from boot camp, number of dependents, boot camp pay grade, race, single status, or the quarter in which the recruit went to boot camp. There was a relationship between military affiliation and a recruit's being female, Hispanic, or not a U.S. citizen. In general, military affiliation did not have an unexplainable significant effect on responses to the New Sailor Survey. The survey responses as a whole suggest that military affiliation does have an effect on how recruits respond; however, further data collection and analysis is necessary beyond the 2,101 data points in this study. The logistic model showed that bonuses above $15,000 and being male were positive predictors of graduation from boot camp. Furthermore, the more a recruit felt prepared by his or her recruiter, the more likely he or she would graduate from boot camp.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".