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Record W4409617676 · doi:10.1111/jora.70026

“It's all about making room for young people”: A mixed‐method study on adolescents' experiences of social adversity and support for violent radicalization in high schools

2025· article· en· W4409617676 on OpenAlexafffundabout
Diana Miconi, Manon Aigoin, Florence Croguennec, Janique Johnson‐Lafleur, Cécile Rousseau

Bibliographic record

VenueJournal of Research on Adolescence · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsMcGill UniversityUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRadicalizationPsychologyFeelingContext (archaeology)Agency (philosophy)InjusticeFocus groupDevelopmental psychologySocial psychologyTerrorismSociology

Abstract

fetched live from OpenAlex

Abstract This mixed‐method study aims to explore adolescents' experiences of social adversity and how these experiences are associated with support for violent radicalization (VR) within the school context. A total of 1911 students (Mage = 15.7; SDage = 0.98; age range 14–18; 49% girls; 73% Canadian‐born) responded to an online survey and four focus groups were conducted with 17 adolescents. Results indicated that experiences of social adversity are very common and are associated with more support for VR and with feelings of injustice and conflictual communication among youth. A democratic school climate can mitigate the association between school unsafety and support for VR. Schools need to create safe and democratic spaces for youth to express themselves and gain some agency.

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)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.061
GPT teacher head0.469
Teacher spread0.408 · 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 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

Citations2
Published2025
Admission routes3
Has abstractyes

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