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Record W3144739056 · doi:10.3389/ijph.2021.617053

Risk and Protective Factors Associated With Support of Violent Radicalization: Variations by Geographic Location

2021· article· en· W3144739056 on OpenAlexafffundabout
B. Heidi Ellis, Alisa Miller, Georgios D. Sideridis, Rochelle L. Frounfelker, Diana Miconi, Saida M. Abdi, Farah Aw-Osman, Cécile Rousseau

Bibliographic record

VenueInternational Journal of Public Health · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsMcGill University
FundersNational Institute of JusticePublic Safety CanadaU.S. Department of Defense
KeywordsModerationRadicalizationContext (archaeology)PsychologySocial supportMental healthDemographyPublic healthScale (ratio)Test (biology)Clinical psychologyGerontologyMedicinePsychiatrySocial psychologyGeographyTerrorism

Abstract

fetched live from OpenAlex

Objectives: We examine the association between perceived discrimination, mental health, social support, and support for violent radicalization (VR) in young adults from three locations across two countries: Montréal and Toronto, Canada, and Boston, United States. A secondary goal is to test the moderating role of location. Methods: A total of 791 young adults between the ages of 18 and 30, drawn from the Somali Youth longitudinal study and a Canada-based study of college students, participated in the study. We used multivariate linear regression to assess the association between scores on the Radical Intentions Scale (RIS) with demographic characteristics, anxiety, depression, social support, and discrimination. Results: In the full sample, discrimination, age, and gender were associated with RIS scores. When we examined moderation effects by location, RIS scores were associated with depression only in Montréal, and with social support (negatively) and discrimination in Toronto. None of the variables were significant in Boston. Conclusion: These findings suggest that an understanding of risk and protective factors for support of VR may be context-dependent. Further research should take into consideration local/regional differences.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.207
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.038
GPT teacher head0.337
Teacher spread0.300 · 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 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

Citations14
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueInternational Journal of Public HealthSame topicTerrorism, Counterterrorism, and Political ViolenceFrench-language works237,207