The enemy of my enemy is not my friend : a theory of rebel alliance patterns in civil war
Bibliographic record
Abstract
Civil wars are rarely two-player affairs. Indeed, civil wars often feature several distinct rebel organizations contesting an incumbent’s territorial control, and while it would seem efficient for these rebel groups to ally with one another against the incumbent, the opposite occurs with surprising frequency: distinct rebel groups regularly fight one another even as they fight the same incumbent. I offer a simple theory that aims to explain why insurgent groups fighting the same incumbent will ally in some instances, but not in others. I argue that when an incumbent boasts military capability sufficient to credibly threaten the elimination of the opposition, rebel groups will be more likely to ally with each other in order to avoid destruction. However, when rebel groups do not fear elimination, they are less likely to ally and more likely to fight amongst themselves, even as they continue their campaigns against the incumbent. There are two reasons that these groups will fight each other: (1) in order to decrease the number of potential bargaining partners for the incumbent or a key sector of the civilian population, and (2) to avoid being disadvantaged when it comes time to divide valuable war spoils, especially when those spoils are won by supplanting the incumbent. I demonstrate the empirical plausibility of this theory with three well-documented cases, and conclude with suggestions for future research on the topic of internecine targeting between rebel groups.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".