Bibliographic record
Abstract
This dissertation develops and tests a new theoretical synthesis for understanding how armed groups keep their combatants fighting rather than deserting or defecting. It examines two basic methods of limiting desertion: keeping coercive control over combatants, and fostering norms of mutual cooperation among them. It argues that the effectiveness of each approach is conditioned by the degree to which combatants value the common aim of the success of the armed group. Norms of cooperation require a commitment to this common aim to be effective. Control can be effective even when combatants are uncommitted, but loses effectiveness with severe disagreements among combatants. This approach provides an advance on past work on the requirements for armed groups in civil wars. Some assume, unrealistically, that common aims drive individual behaviour directly. Others focus exclusively either on individual rewards and punishments or on norms of cooperation. This dissertation, in contrast, sees each as important and as contingent upon the prior consideration of whether combatants share a common aim.A qualitative analysis of armed groups in the Spanish Civil War examines micro-level evidence about common aims, the provision of control, and the emergence of norms of cooperation. The dissertation then tests its major hypotheses statistically using two original datasets of soldiers from that war, based on the author's archival research. It conducts further statistical tests against a new dataset of defection from government armies in 28 civil wars during the 1990s. It concludes with a discussion of new directions.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".