Mandatory Sentencing and the Role of the Academic
Bibliographic record
Abstract
The 1990s witnessed an increase in the number of mandatory sentences created around the common law world. Australia was part of this trend and along with England1 adopted versions of the United States three strikes laws. Canada also passed a record number of mandatory sentences between 1982 and 1999.2 In Australia during the 1990s, mandatory sentencing laws for property offences were enacted in Western Australia and the Northern Territory in response to a moral panic based on a perception that the criminal justice system was not taking victims rights seriously, and that sentencing courts were passing inconsistent and excessively lenient sentences as a consequence of taking into account factors such as race and socioeconomic deprivation.3 In response, from at least 1998 and through to 2002, there was a plethora of journal articles, conference papers, book chapters, reports and other commentary addressing the issue of mandatory sentencing. The torrent of publications has slowed to a trickle but the themes underlying the debate, namely discriminatory sentencing practices and legislative attempts to promote both consistency and harsher sentences remain.
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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 | 0.032 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 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".