Integrated defence workforces: Challenges and enablers of military–civilian personnel collaboration
Bibliographic record
Abstract
Abstract Defence organisations are unique in that they comprise integrated military and civilian personnel working in partnership with each other (e.g., in headquarters, on bases, on missions, in academic settings). Many defence civilians are supervised by military supervisors and managers, while others are themselves responsible for managing military personnel. At the same time, despite often high levels of partnership and integration, military and civilian personnel are governed by very different personnel management systems, and have distinct cultures. These factors can affect the nature and quality of the collaboration and influence personnel outcomes and organisational effectiveness. Indeed, defence organisations are increasingly recognizing the importance of optimizing integration between their military and civilian workforces, with many adopting organisational terms implying that the military and civilian workforces form a cohesive whole: the Defence Team (Canada), the Whole Force Concept (United Kingdom), One Defence Team (Sweden), and Total Defence Workforce (New Zealand). This paper presents results from the Military–Civilian Personnel Survey (MCPS), which was administered in 11 nations as part of a NATO Research Task Group on the topic of military-civilian personnel collaboration and integration (NATO STO HFM RTG-226). This survey was the first systematic examination of large samples of military and civilian respondents, and the first to examine military–civilian relations from the perspective of both military and civilian personnel. The results presented here are based on three open-ended questions included in the survey, which asked respondents to identify 1) the most important factors for establishing and maintaining positive military-civilian personnel work culture and relations, 2) the challenges of working in a military-civilian environment, and 3) the main advantages of working in a military-civilian environment. Results of 5 nations, including Canada, Netherlands, New Zealand, Sweden, and the United Kingdom ( n =1,513 military respondents and n = 2,099 defence civilians) are presented. Results indicate that mixed military-civilian work environments present both unique challenges and advantages, and identified the factors considered to be important for enhancing integration and collaboration between military and civilian personnel. Given that many cross-national patterns emerged, these findings provide useful insights for enhancing military and civilian personnel integration and collaboration across nations. *Adapted from the material first reported in Goldenberg, I. & Febbraro, A.R. (2018; in publication). Civilian and Military Personnel Integration and Collaboration in Defence Organizations . NATO Science and Technology Organization Technical Report - STO-TR-HFM-226. DOI 10.14339/STO-TR-HFM-226. ISBN: ISBN 978-92-837-2092-8.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".