Personnel policy and defence economics: The relationship between efficiency and costs in the armed forces
Bibliographic record
Abstract
The purpose of this study was to examine the impact of human resources policy on the economic efficiency of the armed forces, with a focus on cost optimisation and combat readiness. The study analysed human resource management models, including contract recruitment, long-term workforce planning, short-term training programmes and rotation, and their impact on economic performance in the armed forces of countries such as the United States, the United Kingdom, Germany, Sweden, Canada, Australia, Israel, France, Italy, Spain, India, Romania, Pakistan, Norway, and Ukraine. Furthermore, a comparative analysis of the defence sector of these countries was conducted, with a special focus on the personnel policy of the Ukrainian defence sector. The study also conducted a SWOT-analysis of these models, which helped to assess their strengths and weaknesses, opportunities and threats to the economic efficiency of the defence sector. Specifically, contract recruitment provides flexibility in staffing levels but can lead to low staff loyalty, while longterm workforce planning promotes stability but requires significant investment. The findings showed that effective career planning, exacting standards of recruitment, regular training, and social support are key factors that contribute to cost optimisation and staff stability. Furthermore, the integration of technological training improves resource efficiency and reduces maintenance costs. The SWOT analysis demonstrated that a hybrid model that combines elements of different approaches ensures adaptability in crisis situations but requires careful management due to the heterogeneity of the workforce. Based on the findings, the study offered recommendations for optimising human resources policy to increase the cost-effectiveness of the armed forces, particularly through flexible recruitment programmes, the use of civilian specialists for non-critical positions, and the integration of reservists to perform support tasks in crisis conditions, with a detailed analysis of their implementation in the Ukrainian defence sector. The findings confirm that a balanced human resources policy allows maintaining combat readiness while reducing overall defence sector costs
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".