Bibliographic record
Abstract
The last quarter century in New Zealand has witnessed enormous social, economic, and cultural change. Criminal justice has not been spared in this process, and the present article considers five key areas of sentencing and penal policy where the pressures to review and reassess have perhaps been greatest. Examined here are the growth of prison populations and the rising intensity of penal measures, the reinvention of rehabilitation as a core goal of correctional programming, the increasing use of diversion from the formal processes ofjustice and new trends of experimentation with novel forms of diversion, the rise and rise of the importance accorded to victims of crime (both as individual victims and jointly as stakeholders in the criminal justice process), and, finally, the efforts undertaken over a long period to address concerns about consistency and coherence in sentencing while recognizing the special place of discretion in New Zealand's sentencing culture. Overall, the impact of changes and developments in these areas has been uneven. It is not possible, therefore, to characterize this period as one of penal modernization. A number of countervailing trends, such as political pressure for regressive measures directed toward particular groups of offenders, have meant that innovations must sit side by side with the tired and often discredited approaches of earlier eras.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".