Bibliographic record
Abstract
This article examines the impact of military unit composition on desertion in civil wars. I argue that military units face an increased risk of desertion if they cannot develop norms of cooperation. This is a challenging task in the context of divided and ambiguous individual loyalties found in civil wars. Norms of cooperation emerge, above all, from soldiers sending each other costly signals of their commitment. Social and factional ties also shape these norms, albeit in a more limited fashion. Hence, unit composition can serve as an intervening variable explaining how collective aims can sometimes induce individual soldiers to keep fighting. Analyzing original data from the Spanish Civil War (1936–1939), I demonstrate that three characteristics of a military unit's composition—the presence of conscripts rather than volunteers, social heterogeneity (whose effect is found to be limited to volunteer units), and polarization among factions—increase the individual soldier's propensity to desert. Unit composition proves at least as important as individual characteristics when explaining desertion. This analysis indicates the usefulness of moving beyond commonly used atomistic understandings of combatant behavior. Instead, it suggests the importance of theoretical microfoundations that emphasize norms of cooperation among groups of combatants.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".