MétaCan
Menu
Back to cohort
Record W2904268460 · doi:10.4324/9781315620978-5

Policing persons with mental illness

2017· book-chapter· en· W2904268460 on OpenAlexaboutno aff
Helen Punter

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsnot available
Fundersnot available
KeywordsMental illnessPsychologyPsychiatryMental health

Abstract

fetched live from OpenAlex

This chapter analyses police practices such as police apprehension powers and specialised intervention programs as instances of preventive justice. Police apprehension powers and specialised intervention programs can be critiqued as a deprivation of basic human rights, as outlined in the Convention on the Rights of Persons with Disabilities (CRPD), and for creating disadvantage for those exposed to them. These common law jurisdictions have similar histories regarding the movement of &s;persons with mental illness&s; (PMI) from institutions into the community for treatment and care. The chapter considers whether such laws and programs can be beneficial to PMI, such as through facilitating access to treatment and mental health services and reducing contact with the criminal justice system, thus reducing the &s;criminalisation&s; of PMI. In common law jurisdictions such as Australia, Canada, the United Kingdom and the United States, mental illness is a high-profile issue, and the interaction between the police and PMI has gained particular attention.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.005

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 head0.055
GPT teacher head0.379
Teacher spread0.324 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicHealthcare Decision-Making and RestraintsFrench-language works237,207