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Record W2795932051 · doi:10.22215/etd/2014-11338

The Insurgent's Dilemma: A Theory of Mobilization and Conflict Outcome

2014· dissertation· en· W2795932051 on OpenAlexaff
Eric Jardine

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsCarleton University
Fundersnot available
KeywordsInsurgencyMobilizationDilemmaOutcome (game theory)Political sciencePolitical economyState (computer science)PopulationSociologyPoliticsEconomicsLawComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.022
Scholarly communication0.0060.007
Open science0.0010.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.036
GPT teacher head0.349
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2014
Admission routes1
Has abstractyes

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