A Winning Team of Losers: The Logic of Jihadist Coalitions in Civil Wars
Bibliographic record
Abstract
Abstract For small groups fighting in multi-actor civil wars, joining a larger coalition is often a way to survive. Yet, it is not only rebel or pro-government non-state armed groups that form alliances; in many cases, jihadists have been surprisingly successful in building winning coalitions in civil wars. This is puzzling because jihadists attract fierce international opposition and are therefore very risky teams to join. Jihadists are also typically excluded from the political process, which means that they are unlikely to enjoy the spoils of a peace agreement. Why then would any local groups choose to join jihadist coalitions, rather than other rebel or pro-government coalitions in a conflict theatre? In this paper, we argue that ideology fails to explain this choice; rather, we contend that competition among rebel and pro-government coalitions inevitably produces winners and losers. Under these conditions, jihadists serve as an attractive spoiler coalition, drawing support from groups that see no chance of benefitting from an existing or future peace agreement. By offering these ‘losers’ a wider network and reference group, jihadists can expand their coalition base and territorial reach. By courting support from marginalized groups across ethnic and tribal lines, jihadists can create a winning coalition out of a diverse mix of losers.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.030 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".