Understanding the impact of bail refusal on the Australian public health system
Bibliographic record
Abstract
Australia’s incarceration rates are the highest they have been in a century. Bail and remand contribute much to this trend, and yet the reasons why police refuse bail to vulnerable people are currently unclear. What is clear, though, is that a disproportionate number of vulnerable people are being refused bail, resulting in periods of remand incarceration which end up either longer than the prison sentence given by a magistrate, or undue if the alleged offender is found not guilty. This tendency is particularly observable for the most vulnerable: Aboriginal people, children, people with a mental health condition, the homeless, and women. The authors investigated how magistrates grant or refuse bail as part of the court process, then looked at two tipping points bracketing the bail continuum: 1) policing interactions leading to court appearance, and 2) the impact of bail refusal on public health and community safety and well-being in general. In the present article, they examined how authorized police officers consider refusing or granting bail. This new project aims to investigate the police bail decision-making process and generate new knowledge about the impact of bail refusal on vulnerable people. Through an iterative process with national practitioners and international experts, the authors aimed to identify factors to consider when bail involves vulnerable people. Expected outcomes included the development of mechanisms to benefit the full remit of criminal justice, reduce costs, and improve fairness, accountability, and procedural justice.
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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 | 0.012 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".