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Record W4236814944 · doi:10.1017/cbo9781139060936.008

Education and Ethnic Violence

2012· book-chapter· en· W4236814944 on OpenAlexaff
Matthew Lange

Bibliographic record

VenueCambridge University Press eBooks · 2012
Typebook-chapter
Languageen
FieldSocial Sciences
TopicPeace and Human Rights Education
Canadian institutionsMcGill University
Fundersnot available
KeywordsEthnic groupEthnic violenceCriminologyPolitical scienceSociology

Abstract

fetched live from OpenAlex

Throughout this book, I explore the impact of education on ethnic violence. Using a mixed-methods design that combines cross-national statistics with comparative-historical analysis, I provide consistent evidence that education contributes to ethnic violence. First, the statistical analysis finds that education increases the risk of ethnic violence, especially in environments with ethnic diversity, resource scarcity, and ineffective political institutions. The statistical analysis also offers evidence that the relationship between education and ethnic violence is not driven by the impact of ethnic violence on educational expansion. Next, through a comparative-historical analysis using pattern matching, process tracing, and narrative comparison, I highlight sequences showing that educational expansion precedes ethnic violence, provide evidence that educated individuals commonly organize ethnically violent movements and actively participate in violence, and highlight mechanisms linking education and ethnic violence. The comparative-historical analysis therefore reinforces the findings of the statistical analysis and offers important new insight that helps explain why education is positively related to ethnic violence. All in all, the findings suggest that popular beliefs about the impact of education on peace and tolerance are one-sided and must be reconsidered. The analysis highlights four mechanisms through which education can contribute to ethnic violence. Through the mechanisms, education shapes both the motivations and capacities of individuals to organize and participate in violent ethnic movements. Through the socialization mechanism, education shapes how people perceive themselves, others, and the appropriateness of relations with ethnic others. The analysis fails to support strong constructivist views suggesting that educational socialization can construct identities from scratch and create intercommunal animosity when none existed previously. Instead, I find that education can strengthen, legitimize, and popularize preexisting views and divisions, and that these effects – while less influential than the strong constructivist position claims – still contribute to ethnic violence. Second, I find that education increases the expectations and assertiveness of individuals. Thus, in environments that offer limited opportunities for the educated to meet their expectations, they are at a heightened risk of frustration and aggression. Third, education can place individuals at a heightened risk of intercommunal competition. Competition is often intense for white-collar jobs and political power, and the educated compete for both. As a consequence, the educated are more likely to use violence as a means of eliminating ethnic rivals. More directly, people commonly compete over access to schools and control of the curriculum. The mobilization mechanism is the fourth and final mechanism linking education and ethnic violence. I find that education provides several resources that individuals commonly use to mobilize ethnic violence.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.002

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 head0.044
GPT teacher head0.272
Teacher spread0.228 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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