Social and individual grievances and attraction to extremist ideologies in individuals with autism: Insights from a clinical sample
Bibliographic record
Abstract
Addressing the lack of empirical data on autistic individuals referred to clinical services because of concerns about violent extremism (VE), this paper sketches a portrait of autistic patients referred to a specialized clinical team dealing with VE in Montreal (Canada). We draw on a mixed methods concurrent triangulation design to complement a quantitative file review with qualitative data from focus groups with clinicians. Results highlight the role of isolation, stigmatization, and social grievances as risk factors. They also emphasize the role of education, law enforcement, and justice-system professionals who frequently miss or misinterpret specific features of autism, leading to problematic risk assessments and interventions with further risks of stigmatization, trauma, and disengagement from services. We suggest preliminary avenues to improve intervention for autistic individuals displaying interests for VE. Addressing social isolation and promoting environments adapted to neurodiversity could decrease despair and prevent attraction to extremist discourses. Better collaboration between the different sectors involved in prevention could promote better adapted, less stigmatizing interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".