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Record W3122895639

Scriptural legitimation and the mobilisation of support for religious violence: experimental evidence across three religions and seven countries

2020· article· en· W3122895639 on OpenAlexaff
Ruud Koopmans, Eylem Kanol, Dietlind Stolle

Bibliographic record

VenueEconStor Open Access Articles · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicReligion and Society Interactions
Canadian institutionsMcGill University
Fundersnot available
KeywordsReligious violenceRadicalizationJudaismFaithSociology of religionLegitimationIndoctrinationSociologyPower (physics)FundamentalismIslamCriminologyGender studiesPolitical scienceTerrorismLawTheologyPoliticsIdeologySocial science
DOInot available

Abstract

fetched live from OpenAlex

In their attempts to mobilise supporters and justify their actions, violent religious extremists often refer to parts of scripture that legitimize violence against supposed enemies of the faith. Accounts of religious extremism are divided on whether such references to scripture have genuine motivating and mobilising power. We investigate whether references to legitimations of violence in religious scripture can raise support for religious violence by implementing a survey experiment among 8, 000 Christian, Muslim and Jewish believers in seven countries across Europe, North America, the Middle East and Africa. We find that priming individuals with isomorphic pro-violence quotes from Bible, Torah or Quran raises attitudinal support for religious violence significantly. Effect sizes are particularly large among those with a fundamentalist conception of their religion. Our results show that religious scripture can be effectively used to mobilise support for violence. The findings thus mark a counterpoint to theoretical arguments that question the causal role of religion and have important implications for de-radicalization policies.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
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 head0.109
GPT teacher head0.438
Teacher spread0.329 · 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 designQualitative
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
Published2020
Admission routes1
Has abstractyes

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