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Record W4225985203 · doi:10.1142/s2737436x22500030

Decentralised Terrorism, Religion, and Social Identity

2022· article· en· W4225985203 on OpenAlexaff
Mukesh Eswaran, Hugh M. Neary

Bibliographic record

VenueJournal of Economics Management and Religion · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTerrorismIdentity (music)Altruism (biology)Political sciencePolitical economySocial identity theoryState (computer science)Collective identitySocial psychologySociologyLaw and economicsSocial groupPoliticsLawPsychologyComputer science

Abstract

fetched live from OpenAlex

This paper offers a theory of decentralised, non-state-sponsored terrorism that is characteristic of contemporary reality, and that explains the rise of homegrown terrorism. We argue that the sense of social identity based on religion or shared history is a prime motivator of non-strategic terrorist activities, and we investigate its consequences and implications for defence against terrorism. Terrorist responses to perceived affronts to identity increase with altruism towards in-groups and with endogenous intensity of hate towards out-groups. We show that, while out-group spite is the more essential feature of identity pertinent to decentralised terrorism, the intensity of terrorist actions is magnified by in-group altruism because it plays an important role in overcoming the potential free-riding of terrorists. This makes individual terrorist activities possible without coordination. We use our formulation to provide an alternative explanation for why counterterrorism measures intended to deter and manage terrorism often fail, and frequently can have a backlash effect of actually increasing it. Our results point to the need for Western democracies to reformulate their foreign policies to take account of the role these policies play in instigating and managing contemporary terrorism.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.016
GPT teacher head0.272
Teacher spread0.256 · 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 designNot applicable
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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Economics Management and ReligionSame topicTerrorism, Counterterrorism, and Political ViolenceFrench-language works237,207