The prevalence of mental illness within the Indian criminal justice system
Bibliographic record
Abstract
[Extract] In recent years, the provision of social work services has become an increasingly indispensable element within the criminal jus- tice system (Treger & Allen, 2007), most notably in countries such as the United States, Canada and Scotland (Champagne & Felizardo, n.d.; Scottish Government, 2010; Treger & Allen, 2007). Variously labelled as criminal justice, correctional or forensic social workers (Wilson, 2010), these practitioners now represent one of the foremost providers of bio-psychosocial services to offender populations and victims of crime in these countries. Whether in collaboration with other criminal justice professionals-the police, prison wardens, probation/parole officers-or with colleagues who specialise in diverse fields such as housing, employment, child welfare, mental health, clinical or education, social work intervention and support is now regarded as fundamental in frontline services as well as administrative functions. These specialist skills may be utilised in a number of ways, for example, in adult prisons, youth detention centres, The Prevalence of Mental Illness within the Indian Criminal Justice System 51 probation and parole agencies, the courts, community-based non-profit or non-governmental organisations (NGOs), faith- based agencies as well as primary health and behavioural clinics that serve low socio-economic status clients, many of whom themselves are ex-convicts (Wilson, 2010, p. 1).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".