Bibliographic record
Abstract
Although as noted in chapter 3, community custody aims to achieve multiple sentencing aims, home confinement regimes have usually been introduced in order to reduce the number of prisoners in custody (e.g. Law Reform Commission of New South Wales, 1996; Daubney and Parry, 1999). This chapter explores a critical question regarding community custody: can the sanction actually achieve this goal? Will creation of this sentence result in a widening of the net, as a result of being applied to offenders who otherwise have received a non-custodial sanction? The experience with some other alternatives to imprisonment has been disappointing – the trends with respect to the use of imprisonment reviewed in chapter 2 attest to this fact. However, the limited data regarding community custody are more positive. In jurisdictions such as New Zealand it is too early to know whether community custody is an effective tool to reduce the number of admissions to custody. In these countries, community custody either is too new an innovation, or has not been sufficiently widely implemented to make a difference to custodial populations. The experience in Canada and Finland yields the clearest (and most positive) findings in this regard, and accordingly will be explored in more detail. Although no comprehensive international review has been conducted, researchers have concluded that there is little evidence that decarceration is a consequence of the creation of a community custody sanction.
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 | 0.003 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.001 |
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".