Replication Data for: Crowding Out the Field: External Support to Insurgents and the Intensity of Inter-rebel Fighting in Civil Wars
Bibliographic record
Abstract
How does external support to insurgents influence the likelihood that the latter will get involved in violent clashes against other rebel groups? In this article, we outline a theoretical framework which contends that, in multiparty civil wars, rebels sponsored by foreign states are more likely to participate in high-intensity inter-rebel conflicts than rebels receiving no support from external states. We argue that this is because external support creates strategic incentives for insurgent leaders to target other rebel contenders in order to signal resolve to their sponsors and to crowd out the battlefield ahead of the post-conflict period. External support, moreover, tends to activate potent socio-psychological mechanisms among rank-and-file combatants that may remove restraints on the use of violence against other rebel fighters. Using data on inter-rebel conflicts from 1989 to 2018, we test these hypotheses with a set of large-N regressions and find strong support for our theory. Further analyzes inductively reveal that our statistical results are likely, to some extent, to be driven by the prevalence of religious insurgencies in contemporary conflicts. Religious insurgencies display organizational features that could reinforce vertical strategic incentives and horizontal socio-psychological dynamics, thereby increasing their involvement in inter-rebel fighting. To further probe the ‘meso-foundations’ of inter- rebel fighting following rebel sponsorship, we then provide qualitative evidence on the Syrian Civil War. Our article contributes to scholarship by highlighting the consequences of external support on conflict processes beyond the insurgent-incumbent dyad.
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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 | 0.015 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.256 | 0.076 |
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".