Bibliographic record
Abstract
310): 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.Throughout the development of the mobilization and conflict outcome theory, I substantiate each point with qualitative historical evidence from a number of insurgencies.I also explicitly test three hypotheses that are derived from the theory using descriptive statistics from 21 insurgencies.Finally, I test the proposed mechanisms 6 This discussion is drawn from Gordon H. McCormick, Steven B. Horton, and Lauren A Harrison, "Things Fall Apart: The Endgame Dynamics of Internal Wars," Third World Quarterly, Vol. 28, no. 2 (2007), 321-367.7 The Internal War database uses a fairly low threshold of 25 battle deaths for inclusion in the sample.See Ibid., 324.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".