Analysis of Officer Retention and Success in the US Army by Commissioning Source
Bibliographic record
Abstract
There are four primary paths to commission as an Officer in the US Army. The most common way of commissioning is through the Army Reserve Officers’ Training Corps (ROTC), which allows students to enroll in elective leadership and military courses at colleges and universities. Then, at graduation, ROTC Cadets are commissioned as Second Lieutenants. The second most common route is through the United States Military Academy at West Point (commonly referred to as USMA or West Point), where Cadets are immersed in military customs and traditions while working toward a college degree and upon graduation are commissioned as Second Lieutenants. The third most popular route is Direct Commissioning, which provides individuals with specialized skills in professional fields like law, medicine and religion an immediate way to apply their skills in the Army, these Officers usually begin their Army careers as Captains. Whereas the least common path to becoming an Army Officer is through Officer Candidate School (OCS), an officer training school which trains applicants who are already college graduates on the skills necessary to become a US Army Second Lieutenant. This project sets out to determine if commissioning source can be used as a predictor for Officer retention. It will analyze the general traits of Officers coming from each commissioning source and provide an explanation for inequalities amongst commissioning sources. Equipped with this academic information I will also interview officers of various ranks in order to build a profile of the successful Officer who aims to make the Army a career. I will use my findings to create a road map in order to be as successful an Officer as I possibly can be.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".