“Between the self and the other”: clinical presentation of male supremacy in violent extremists
Bibliographic record
Abstract
This paper examines the relationship between gender and violent extremism (VE) among individuals engaged in VE clinical services in Montreal, Quebec (Canada). We use mixed methods to understand the experiences and characteristics of individuals who express support for male supremacist ideologies. Study participants include 86 patients enrolled in VE clinical services and 7 clinical practitioners providing services. We conduct a retrospective chart review to identify clinical and sociodemographic characteristics of male supremacists. A focus group was conducted with members of the clinical team. Integrating quantitative and qualitative findings provides an opportunity to draw meta-inferences on male supremacist violent extremists, including a typology of the phenomena as well as clinical characteristics and social dynamics. Clinicians articulated that many of the harmful attitudes and beliefs of male supremacists were not marginal, but rather reflected in everyday forms of misogyny, homophobia, and transphobia that were activated by their personal experiences. Our findings suggest the importance of clinicians remaining attentive to the underlying gendered grievances which shape a range of extremist beliefs. Finally, we explore the value of training practitioners who work on VE on diverse domains of gendered violence which may intersect with VE participation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".