Bibliographic record
Abstract
Why do some rebel groups win while others lose? Current explanations for why the outcomes of insurgencies vary tend to either overlook the role that the population plays in supporting an insurgency or fail to specify how the requirements of a rebel group's mobilization of popular support might have their own effect on conflict outcome. In this dissertation, I develop a theory of conflict outcome that links a rebel group's mobilization of popular support to organizational and administrative reforms, which, in turn, affect an insurgency's chances of winning or losing. I argue that all rebel groups face a core "Insurgent's Dilemma." On the one hand, insurgent groups often need to become organizationally centralized and have a large-scale administrative presence in order to mobilize a large amount of popular support, because with higher levels of popular support comes a greater chance of defeating a regime. Yet the same organizational and administrative characteristics that allow for effective mobilization actually favour the state in its efforts to destroy an insurgency. On the other hand, an insurgency can abjure centralization and a large-scale presence and avoid adopting characteristics that favour the state's counterinsurgency efforts. Doing so, however, limits the amount of popular support that an insurgency can ultimately mobilize, which effectively means that the group is unlikely to succeed, even if it is hard to defeat.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".