Autism spectrum disorder : understanding and management through countering violent extremism strategies : a thesis presented in fulfilment if the requirements for the degree of Master of Health Science in Psychology at Massey University, New Zealand
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterised by persistent social and cognitive deficits presenting in adolescent developmental phase. There are three categories of impairment that can occur; language skills, social behaviour, and cognitive functioning, which can lead to atypical interpretations of surrounding environments. Although expression of ASD characteristics varies across individual circumstances, common behaviours identified across the cohort are suggested to influence perceptions of social law and consequences, and susceptibility to radicalisation to violent extremism. Violent extremism (VE) is a global problem that has led countries such as New Zealand, Australia, United Kingdom, and Canada, to develop Counter-Violent Extremism strategies to minimize the impact of VE. The current research explores how these strategies attend to the specific needs and impairments of autistic individuals. Chapter one explores autism and violent extremism and how they may link. Examination is made of the current understandings around autism and how this neurodevelopmental disorder may be linked with expressions of violent extremism. Chapter two provides an account of the chosen methodology of Document Analysis, the analysis processes undertaken and the ethical considerations. Chapter three provides the results of the study, structured by way of themes and sub-themes found across the dataset. The final chapter consists of a discussion regarding each theme and how it corroborates with previous research. This chapter will also explore the strengths and limitations that occurred when implementing this study and outlines any recommendations of future research direction.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".