Integrating civilians into military organizations: Linking micro and macro levels of analysis
Bibliographic record
Abstract
LAY SUMMARY The wars in Iraq and Afghanistan changed how civilians are integrated in military units, which has key implications for both personnel and the larger organization. Examining micro-effects is important because they reveal unintended consequences of personnel policies based on macro-level goals and assumptions. This article reviews 15 years of sociological research on micro-level outcomes across several key domains. Extant literature presents consistent findings of negative impacts of civilian integration on social comparisons, retention, cohesion, and mental health. Conversely, mixed results are found on military-civilian (mil-civ) integration on military culture and customs. This article also proposes a novel theoretical model to explain how these micro-effects affect macro-level military readiness. Accordingly, this article provides a framework to organize extant literature and identify new research linking micro-macro levels in military organizations. It is clear mil-civ blended forces produce unintended challenges for military readiness and individual personnel. Moving forward, more research is needed to examine unintended effects based on race and gender representation in a mil-civ blended force. There is much still unknown about the micro-level effects of systematically integrating civilians — for both military and civilian personnel — but what is clear is that it produces numerous unintended challenges for military readiness and individual service members.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".