Exploring sources of employment dissatisfaction and perceptions of organizational fairness in the Canadian Armed Forces
Bibliographic record
Abstract
Introduction: Research has established compelling links between organizational fairness and a variety of desirable human resource outcomes in both military and civilian employment settings. For instance, personnel who feel that they are treated equitably at work report higher levels of personal well-being and lower turnover intentions and are inclined to engage in organizational citizenship behaviours. Despite the importance of this construct, little research has been conducted on the situational antecedents of perceptions of organizational fairness, particularly in military settings. Given the strong association between organizational fairness and career intentions in this context, examining aspects of military employment where perceived breaches in fairness may occur could inform Canadian Armed Forces (CAF) retention and reconstitution efforts. Methods: Leveraging qualitative data from a CAF-wide survey, main themes associated with perceived unfairness across four key employment aspects (postings, career management, performance evaluations, and promotions) were identified through thematic analyses. Results: Substantial similarities were evident in the themes across employment aspects: a large proportion of concerns over both career management and posting unfairness centred on a lack of input by members, the mistreatment of personnel, and inadequate career management; similarly, fairness concerns over performance evaluations and promotion decisions centred on a lack of objectivity, the underweighting of competence, and the emphasis on criteria unrelated to one's occupation. Discussion: Implications for culture change efforts, employee well-being, and retention are discussed, as well as directions for future research.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".