Policing along the spectrum: Reducing risk and improving service delivery
Bibliographic record
Abstract
Autism spectrum disorder is a complex life-long neurodevelopmental disorder that affects social skills, language, learning, cognitive function, physical abilities, and behavior. Moreover, autism is becoming increasingly prevalent in Canada and abroad. Inevitably, law enforcement professionals will interact with the autistic population in their duties; however, many exchanges produce poor outcomes. Physical restraint is often used, and the benefit of autism-trained Crisis Intervention Teams is underutilized, especially in Canada. In addition, current training outcomes for law enforcement professionals are ephemeral and unlikely to yield mastery. This article argues three antidotes to improve service delivery and reduce risk: Crisis Intervention Teams that incorporate autism training; methodologically sound and rigorously evaluated practical training; and community outreach, which creates relationships proactively and fosters trust. Future research should: analyze the outcomes of autistic-trained Crisis Intervention Teams; determine qualitatively what the population with autism and their stakeholders seek in a comprehensive training curriculum; and determine the success of nascent training courses and community outreach goals.
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 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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".