Self‐Enhancement in Military Leaders: Its Relevance to Officer Selection and Performance
Bibliographic record
Abstract
We report on two studies in which we measured army cadets' tendencies to engage in two types of self‐enhancement: communal self‐enhancement (a moralistic bias in self‐presentation) and agentic self‐enhancement (an egotistic bias in self‐presentation). These self‐presentation styles were then related to their selection and performance as military leaders. In Study 1, scores on self‐enhancement questionnaires were used to predict selection decisions for 206 applicants to an army officer training program. We found that applicants who were higher in either communal or agentic self‐enhancement were more likely to be accepted for leadership training. In Study 2, we evaluated peer and superior ratings of 94 military cadets' leadership, reflecting leadership emergence and leadership effectiveness, respectively. We found that communal self‐enhancement negatively predicted leadership emergence, with those ratings becoming more negative over a 3‐year time‐span, whereas agentic self‐enhancement positively predicted leadership effectiveness. Our results imply that, at least in the present military context, people making selection decisions should be particularly aware of the relations between (a) applicant self‐enhancement tendencies and those decisions, and (b) high communal self‐enhancement in officer trainees and negative evaluations by their cadet peers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".