Imprisonment in Australia: The Offence Composition of Australian Correctional Populations, 1988 and 1998
Bibliographic record
Abstract
Between 1988 and 1998, the number oi people in Australian prisons increased from 12,321 to 19,906, an increase of 62 per cent. This paper takes Prison Census figures and analyses the offences for which people were gaoled in 1988 and 1998. It examines trends in the offence composition of Australian prison populations by age, gender, and jurisdiction. In 1988, 7.5 per cent of the total prison population was imprisoned for assault and, in 1998, this figure had increased to 12.6 per cent. It is an increase of 5.1 percentage points. At the same time, in 1988 a quarter (25%) of the prisoner population was imprisoned for break and enter or theft, while in 1998 this proportion had fallen to less than one- fifth (19.1%). For females, there was an increase in the proportion gaoled for assault from 3.4 per cent in 1988 to 10.4 per cent in 1998, while at the same time the proportion gaoled for drug offences fell from 16.1 per cent to 11.8 per cent. Prisoners in the 20-34 age groups increased their contribution to the total of inmates held in prison for assault. Older prisoners were held for sex offences more than for any other offence. Of prisoners aged 50-64, 38.9 per cent were gaoled for sex offences in 1998, compared to 18.5 per cent in 1988, and for those aged 65 and over, 56.9 per cent were gaoled for sex offences compared to 23.1 per cent in 1988. By breaking down the offence composition in this way we can learn about changes over time and help structure responses for prison services. Adam Graycar Director The offence composition of a prison population is an imperfect representation of the structure of crime in society. There are several reasons for such a discrepancy. Police come to know only about a fraction of all crimes. Data from the National Crime and Safety Survey conducted in 1998 show that respectively, 74 per cent and 30 per cent of (most recent) incidents of household and personal offences were reported to police (Australian Bureau of Statistics (ABS) 1999). Police do not record all the reported crime incidents. For example, it has been found that in Queensland a criminal offence report was completed for around one-third of the calls attended by police, but many of these calls may have been related to non-criminal matters (Criminal Justice Commission 1996). Further, only a minority of recorded crimes is cleared by charge. For example, in New South Wales, 18.5 per cent of residential break and enter, motor vehicle theft, assault, and robbery offences recorded by police in 1996 were cleared by the arrest or identification of suspects. The same data show that 57 per cent of offenders charged with these offences were convicted, and that 15 per cent of these offenders were given prison sentences (Mukherjee and Reichel 1999). Despite these limitations, the type of offences for which prisoners are either remanded or sentenced may affect the size of prison populations via the impact it has on both the rate at which persons are admitted to prison and the time they spend there. This is the result of interrelated factors such as: * sentencing decisions associated with the seriousness of crimes known by the courts; * bail legislation, remission and parole legislation, as well as policy and practice; * the criminal history of those coming through the courts; * public perceptions about crime and punishment that may lead to legislative changes; * patterns of police activity; and * trends and patterns of criminal activity. The study of the offences for which individuals are in prison provides information about the evolution of prison populations over time and their difference across jurisdictions. A brief overview of the effect of admission rates upon the size of prison populations can be found in Carcach and Grant (1999). This study aims to identify the major features of the offence composition of Australian prison populations using data collected as part of the prison censuses conducted by the Australian Institute of Criminology and the Australian Bureau of Statistics in 1998. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".