Profile of violent behaviour by inmates in NSW correctional centres.
Bibliographic record
Abstract
Inmates who display violent behaviour present significant management and security demands on correctional centres in NSW and pose a potential risk of harm to other inmates, correctional staff and to the general community. The current bulletin provides a preliminary snapshot of the number of sentenced inmates in NSW correctional centres who have displayed violent behaviour and profiles the demographic and criminogenic characteristics of these inmates. It was found that more than half of all inmates serving a sentence of full-time custody on 20 March 2005 are currently convicted of a violent offence or have displayed violent behaviour whilst in custody. Although the majority of these inmates are male, over a third of the sentenced female inmate population are also incarcerated for a violent offence or institutional violence. The most common offence types for which the profiled inmates are serving a sentence of fulltime custody were assault related offences and a quarter of the profiled inmates have violently breached correction centre regulations during their current sentencing period. Potential implications for the management and rehabilitation of inmates with violent offences are discussed with reference to identified gender differences in the pattern of violence. Jennifer Galouzis, Research Officer NSW Department of Corrective Services Corporate Research, Evaluation and Statistics
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".