Radicalization to Violence: A View from Cultural Psychiatry
Bibliographic record
Abstract
This article introduces a thematic issue of Transcultural Psychiatry with selected papers from the McGill Advanced Study Institute in Cultural Psychiatry on “Pluralism and Polarization: Cultural Contexts and Dynamics of Radicalization,” which took place June 20–22, 2017. The ASI brought together an interdisciplinary group scholars to consider the role of social dynamics, cultural contexts and psychopathology in radicalization to violent extremism. Papers addressed four broad topics: (1) current meanings and uses of the term radicalization; (2) personal and social determinants of violent radicalization, including individual psychology, interpersonal dynamics, and wider social-historical, community and network processes; (3) social and cultural contexts and trajectories of radicalization including the impact of structural and historical forces associated with colonization and globalization as well as contemporary political, economic and security issues faced by youth and disaffected groups; and (4) approaches to community prevention and clinical intervention to reduce the risk of violent radicalization. In this introductory essay, we revisit these themes, define key terms, and outline some of the theoretical and empirical insights in the contributions to this issue. Efforts to prevent violent radicalization face challenges because social media and the Internet allow the rapid spread of polarizing images and ideas. The escalation of security measures and policies also serves to confirm the worldview of conspiracy theory adherents. In addition to addressing the structural inequities that fuel feelings of anger and resentment, we need to promote solidarity among diverse communities by building a pluralistic civil society that offers a meaningful alternative to the violent rhetorics of us and them.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.090 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".