Comment s’explique la radicalisation violente ?
Bibliographic record
Abstract
La radicalisation s’est imposée au rang des priorités ces dernières années à l’échelle internationale. Pour autant, ce phénomène reste encore largement incompris, étant par essence subjectif. Alexandre Chevrier-Pelletier et Pablo Madriaza, chercheurs à l’International Centre for the Prevention of Crime (ICPC), basé à Montreal, présentent dans cet article les enseignements majeurs de la revue systématique réalisée par l’ICPC sur la prévention de la radicalisation. Fondée sur l’analyse de 483 documents de recherche en matière de radicalisation islamiste et d’extrême droite, elle vise à identifier des facteurs communs aux trajectoires de radicalisation violente. Les auteurs en concluent que ces études souffrent d’une trop faible prise en compte des contextes locaux, et donc de leurs spécificités, dans l’analyse des facteurs de radicalisation. Remédier à cette lacune est un préalable en vue de concevoir une politique de prévention efficace.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".