Intrinsic Social Incentives in State and Non-State Armed Groups
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
How do non-state armed groups (NSAGs) survive and even thrive in situations where state armed groups (SAGs) collapse, despite the former’s often greater material adversity? We argue that, optimizing under their different constraints, SAGs invest more in technical military training and NSAGs invest more in enhancing soldiers’ intrinsic payoffs from serving their group. Therefore, willingness to contribute to the group should be more positively correlated with years of service in NSAGs than in SAGs. We confirm this hypothesis with lab-in-the-field and qualitative evidence from SAG and NSAG soldiers in Nepal, Ivory Coast, and Kurdistan. Each field study addresses specific inferential weaknesses in the others. Assembled together, these cases reduce concerns about external validity or replicability. Our findings reveal how the basis of NSAG cohesion differs from that of SAGs, with implications for strategies to counter NSAG mobilization.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it