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Record W107461757

Do Working Men Rebel

2010· article· en· W107461757 on OpenAlexaboutno aff
Jacob N. Shapiro

Bibliographic record

VenueSSRN Electronic Journal · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Conflicts in Afghanistan, Pakistan, and Middle East
Canadian institutionsnot available
Fundersnot available
KeywordsUnemploymentInsurgencyPoliticsGovernment (linguistics)Political scienceYouth unemploymentCorporate governanceAdjudicationOrder (exchange)CriminologyPublic administrationSociologyEconomicsEconomic growthManagementLawFinance
DOInot available

Abstract

fetched live from OpenAlex

Most aid spending by governments seeking to rebuild social and political order is based on an opportunity-cost theory of distracting potential recruits. The logic is that gainfully employed young men are less likely to participate in political violence, implying a positive correlation between unemployment and violence in locations with active insurgencies. We test that prediction in Afghanistan, Iraq and the Philippines, using survey data on unemployment and two newly-available measures of insurgency: (1) attacks against government and allied forces; and (2) violence that kills civilians. Contrary to the opportunity-cost theory, the data emphatically reject a positive correlation between unemployment and attacks against government and allied forces (p<.05%). There is no significant relationship between unemployment and the rate of insurgent attacks that kill civilians. We identify several potential explanations, introducing the notion of insurgent precision to adjudicate between the possibilities that predation on the one hand, and security measures and information costs on the other, account for the negative correlation between unemployment and violence in these three conflicts. † We acknowledge the tremendously helpful comments received at the June 2009 Institute on Global Conflict and Cooperation conference on Governance, Development, and Political Violence, and at seminars at UC Berkley, UCLA, UC San Diego, the University of Southern California, and the University of Ottawa.. L. Choon Wang, Josh Martin, Lindsay Heger and Luke N. Condra provided invaluable research assistance. Gordon Dahl, James Fearon, Esteban Klor, Daniele Paserman, Kris Ramsay, and our anonymous reviewers provided critical comments. We acknowledge grant #2007-ST-061-000001 by the United States Department of Homeland Security through the National Center for Risk and Economic Analysis of Terrorism Events and grant # FA9550-09-1-0314 by the United States Department of Defense through the Air Force Office of Scientific Research. The opinions, findings, and recommendations in this document are the authors‘ and do not reflect views of the United States Department of Homeland Security or Department of Defense. All mistakes are ours. Replication data are available on the authors‘ websites and at http://jcr.sagepub.com/.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.728
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.293
Teacher spread0.278 · 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 teacher head, not a consensus.

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

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

Citations18
Published2010
Admission routes1
Has abstractyes

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