Bibliographic record
Abstract
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/.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.012 |
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".