Are we failing them? : an analysis of the New Zealand criminal youth justice system : how can we further prevent youth offending and youth recidivism? : a thesis presented in partial fulfilment of the requirements for the degree of Master of Arts in Social Policy at Massey University, Auckland, New Zealand
Bibliographic record
Abstract
Youth crime is a prominent social issue in New Zealand that causes emotional and physical harm and loss to the numerous victims. This research provides an analysis of the current youth criminal justice system in New Zealand, beginning with a timeline of the history and evolvement of the youth justice system to illustrate how New Zealand has arrived at the present system. The drivers of youth crime and youth involvement in criminal offending were found to be initially born from a lack of engagement with education; neurological disorders; learning difficulties and mental illness; as well as the impact of young people’s childhood, which can include exposure to family violence; drug and alcohol abuse. \nComparative policy evaluation was applied with comparative methodology and comparative cross national research to undertake an analysis of the youth justice system in New Zealand. International comparisons were used to discover plausible and practical improvements to the current youth justice system in New Zealand. The OECD countries used in the comparative analysis included Canada, Scotland, England & Wales, United States and Austria, who between them have significantly diverse and contrasting youth justice models ranging from welfare, care and protection centred models, to community-based rehabilitation models; preventative education and support to punitive models in their response to youth crime. \nii \nIt was found that several aspects of New Zealand’s current youth justice system function well when compared internationally. However, the comparative analysis also highlighted that New Zealand’s youth justice system presents a problematic gap in both the sheer lack of preventative methods in response to youth offending as well as community support during the rehabilitation stage. \nA number of policy recommendations are included within this report in response to the present shortcomings of the existing youth justice system in New Zealand. These policy recommendations provide practical solutions; adopting a preventative policy focus with plausible improvement suggestions to the existing youth justice system. The objectives are to ameliorate the youth justice system to better support youth offending and youth recidivism.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".