Send lawyers, guns and money: the politics of militia survival in the Middle East
Bibliographic record
Abstract
This dissertation considers the sources of variation in the ability of nonstate military actors to both resist and recover from – in short, to survive – confrontations with much stronger conventional militaries. While much of the existing literature on civil war focuses on structural variables, such as initial material or social endowments, this dissertation argues that these resources are less important in determining a non-state actor's resilience than the relationships it builds in order to acquire them and the means it uses to do so. “Resources” may be either material, (e.g., money and arms) or non-material (e.g., legitimacy and influence) and are acquired (from the civilian population and/or a foreign sponsor) through three possible strategies: coercion, service-provision, and marketing. I argue that the first is least effective, as coercion tends to provide only short-term access to material resources, while marketing is the most effective, as it produces the most durable access to both material and non-material resources. Service provision produces a mid-range outcome. Moreover, all three can have significant unintended consequences. I test the argument by comparing the performances of the PLO, Hizbullah and Hamas in their confrontations with Israel over the past four decades. I conclude by considering the implications of my conclusions both for the study of nonstate actors more broadly, and for the dynamics of 21st century Iraq and Afghanistan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".