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Record W4401463560 · doi:10.1089/aut.2023.0192

Neurodivergence and the Rabbit Hole of Extremism: Uncovering Lived Experience

2024· article· en· W4401463560 on OpenAlexaffabout
Sachindri Wijekoon, John Elder Robison, Christie Welch, Alexander Westphal, Rachel Loftin, Barbara Perry, Victoria Rombos, Christian Picciolini, Catherine Bosy, Lili Senman, Patrick Jachyra, Simon Baron‐Cohen, Melanie Penner

Bibliographic record

VenueAutism in Adulthood · 2024
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsOntario Tech UniversityUniversity of TorontoHolland Bloorview Kids Rehabilitation HospitalWestern University
Fundersnot available
KeywordsIdeologyHatredPsychologySocial psychologyPsychological interventionDevelopmental psychologyPolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

Background: There have been sporadic and disturbing media accounts of autistic people engaging with extreme ideologies, with comparatively little systematic exploration of this suggested association. Existing research has failed to consider the contextual factors that could influence these rare occurrences of engagement with extreme ideologies. This study explores how autistic individuals involved in extreme ideologies describe personal and contextual factors affecting their participation. Methods: Twelve individuals from Canada and the United States who were either diagnosed or self-identified as autistic and have engaged with extreme ideologies participated in semistructured interviews. The research approach and analysis of the data were informed by interpretative phenomenological analysis. Our interdisciplinary team met regularly to collectively examine initial assumptions and interpretations, while maintaining a central focus on the perspectives of the participants. Results: We identified the following three key themes: (1) early wounds, (2) missed formative opportunities, and (3) finding a fit for neurodivergence. Traumatic experiences, disenfranchisement, learned hatred from an insular upbringing, and systemic failings in health and social service systems contributed to participants’ decisions to engage with extreme ideologies. Hate groups, in turn, filled the voids by providing acceptance, purpose, structure, sense of community, and by accommodating participants’ neurodivergent needs. Conclusion: Autism alone did not explain participants’ engagement with extreme ideologies. Trauma and disenfranchisement related to being neurodivergent were common factors that made hate groups more appealing. Proactive interventions to prevent engagement in extreme ideologies must champion inclusive environments that recognize autistic individuals’ skills and address underlying factors that contribute to their disenfranchisement. Community Brief What was the purpose of this study? The media has reported on high-profile cases of autistic people with extreme beliefs who acted in violent ways. There is a lack of research on this topic and researchers have not directly spoken with autistic people who have been involved with these extreme beliefs. Our goal was to understand why some autistic people engaged with hateful beliefs, asking them about both autism and their life circumstances. What did the researchers do? We interviewed 12 people who identified as autistic to understand why they became involved in extreme beliefs. We conducted the interviews by phone or using Zoom Health. We read the interview text, identified important statements, and then identified the ideas linking these statements. What were the results of the study? Most of the people in our study were young to middle-aged men with White/European background from Canada and the United States. Only a few had a formal autism diagnosis. Participants faced many challenges, including being neglected by parents, experiencing trauma, and not feeling like they belong. Many of them were not given opportunities to freely express themselves or have positive interactions with people from different backgrounds. Everyone lacked the opportunity to build a positive view of themselves and the world around them. Participants described some autistic and neurodivergent traits, such as having a focused interest in one topic, having difficulty understanding and connecting with others, preferring clear rules and a set routine, and having difficulty controlling emotions, were not accepted elsewhere but were accepted in groups with extreme beliefs. What do these findings add to what was known? Hate groups provided autistic people a supportive environment where their strengths were highlighted, their individuality was celebrated, and their challenges were accommodated. This level of support contrasted with what autistic people had previously experienced in society. What are potential weaknesses? One limitation of our study was that we included people who identify as autistic, but we did not verify their diagnosis through formal testing. Our sample is a small group of autistic people who had engaged with hateful beliefs; our findings do not apply to all autistic people and should not be interpreted that all autistic people are more likely to have extreme or hateful beliefs. How will these findings help autistic adults now or in the future? Engagement with extreme groups or beliefs is only one possible negative outcome from the lack of inclusion and acceptance of autistic people, but is an important one. We should create supportive environments that welcome and appreciate autistic peoples’ skills and interests to allow them to feel valued and connected to their community. Families, teachers, and professionals should prioritize accurate and timely diagnosis and provide supports that are tailored to their needs. These steps can help autistic people build meaningful relationships and prevent them from turning to extremist groups to meet their needs.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.313
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.302
Teacher spread0.267 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2024
Admission routes2
Has abstractyes

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