Is Military Employment Fair? Application of Social Comparison Theory in a Cross-National Military Sample
Bibliographic record
Abstract
Although military and civilian personnel work closely together in defense organizations, they are subject to different human resources practices and conditions of service. Assessments of military personnel along a range of job characteristics are examined to identify areas in which they assess themselves as “better or worse off” than their civilian counterparts, and how these comparisons relate to perceptions of fairness using data from Belgium, Canada, and the Netherlands. Military personnel reported meaningfulness/support aspects (e.g., meaningful work) as similar for military and civilian personnel, indicated that negative impacts (e.g., risk of injury) were greater for military, and perceived variability in instrumental benefits (e.g., pay, advancement). Upward social comparison (i.e., seeing oneself as worse off) was related to lower perceived fairness, whereas downward social comparison was related to higher perceived fairness. This research informs mechanisms for promoting perceptions of fairness and enhancing military–civilian personnel relations in defense establishments.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".