Core Requirements of a Best Practise Model for Police Encounters Involving People Experiencing Mental Illness in Australia
Bibliographic record
Abstract
A great deal of research has been done in the United States and progress made in assisting police to deal with encounters with mentally ill offenders. Much has been written about crisis intervention teams and their varying degrees of success in the United States, depending on the particular jurisdiction. While the situation there has been well documented in the literature Introduction 39 Methodology 40 The Situation in Western Europe 41 The Perception of Risk 42 A Return to Reinstitutionalisation? 43 The United Kingdom 43 Antistigma Programmes 47 Diversion Schemes 47 The Appropriate Adult 50 A Study of Police Practices and Attitudes in Some Western and Northern European Countries 51 Netherlands 51 Denmark 52 France 52 Norway 54 Greece 54 Central and Eastern Europe 56 Republic of Croatia 57 Conclusion 57 References 58 (Compton et al., 2008), and to a lesser extent the situation in the UK, Canada, and Australia, outside of these areas there is a lack of research literature specifically on this issue. The purpose of this chapter is to provide some coverage of the situation in Europe (including the UK) from such documentation, as is available.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".