Committed sponsors: external support overtness and civilian targeting in civil wars
Bibliographic record
Abstract
Does the overtness of external support to rebels affect civilian targeting in civil wars? Conflict studies increasingly scrutinize how insurgent sponsorships shape rebels’ behavior. However, the influence of external sponsors’ decisions to publicly acknowledge or deny their support on rebel conduct is largely neglected. This article introduces a new dataset on the overtness of external support to rebels in civil wars between 1989 and 2018. It then assesses whether the overtness of support is correlated with insurgents’ propensity to target civilians. I hypothesize that overtly supported rebels are less likely to target civilians than covertly supported rebels. This hypothesis stems from how supply-side factors—the way state sponsors expectedly act after having allocated their support—impact insurgents’ structure of incentives around relations with non-combatants. Statistical analyses yield strong support for my hypothesis. Moreover, further analyses show that support overtness influences civilian targeting independently from sponsors’ characteristics, such as political regimes or foreign aid reliance. Thus, in addition to the type of material aid insurgents receive, variation in whether support is covert or overt shapes how rebels treat civilians.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".