R v Mika: An investigation into the Court of Appeal’s neglect of s 27 of the Sentencing Act 2002
Bibliographic record
Abstract
The Court of Appeal in the case of R v Mika failed to engage with section 27 of the Sentencing Act 2002 in dismissing Mr Mika’s appeal against his sentence. In both the High Court and Court of Appeal the focus was on Mr Mika’s argument for a discount of 10 per cent to be applied to his sentence to reflect his Māori heritage and associated social disadvantages. Section 27 of the Sentencing Act would allow a court to take into account cultural information regarding Maori offenders’ backgrounds and the systemic disadvantages stemming from this. In dismissing Mika’s appeal, the Court erred in not considering the clear signals from Parliament that the judiciary were to take into account Maori offenders’ backgrounds at the sentencing stage through s 27 in an effort to fit appropriate sentences to Maori offenders. Recent developments in Canada have seen the Canadian judiciary recognise their role in the over-representation of Aboriginal people in the Canadian prison population. The New Zealand judiciary can take lessons from the willingness of the Canadian judiciary to take cultural information into account at sentencing.
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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 | 0.062 | 0.173 |
| Meta-epidemiology (narrow) | 0.000 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.048 | 0.011 |
| Scholarly communication | 0.021 | 0.006 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.032 | 0.038 |
| Insufficient payload (model declined to judge) | 0.004 | 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".