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Record W1962702548 · doi:10.3389/fpsyg.2015.01325

Motivation for aggressive religious radicalization: goal regulation theory and a personality × threat × affordance hypothesis

2015· article· en· W1962702548 on OpenAlexafffund
Ian McGregor, Joseph Hayes, Mike Prentice

Bibliographic record

VenueFrontiers in Psychology · 2015
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRadicalizationPsychologySocial psychologyPersonalityAffordanceNarrativeBig Five personality traitsVulnerability (computing)Set (abstract data type)TerrorismCognitive psychology

Abstract

fetched live from OpenAlex

A new set of hypotheses is presented regarding the cause of aggressive religious radicalization (ARR). It is grounded in classic and contemporary theory of human motivation and goal regulation, together with recent empirical advances in personality, social, and neurophysiological psychology. We specify personality traits, threats, and group affordances that combine to divert normal motivational processes toward ARR. Conducive personality traits are oppositional, anxiety-prone, and identity-weak (i.e., morally bewildered). Conducive threats are those that arise from seemingly insurmountable external forces and frustrate effective goal regulation. Conducive affordances include opportunity for immediate and concrete engagement in active groups that are powered by conspiracy narratives, infused with cosmic significance, encouraging of moral violence, and sealed with religious unfalsifiability. We propose that ARR is rewarding because it can spur approach motivated states that mask vulnerability for people whose dispositions and circumstances would otherwise leave them mired in anxious distress.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.035
GPT teacher head0.326
Teacher spread0.292 · 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 designObservational
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

Citations44
Published2015
Admission routes2
Has abstractyes

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