The undercounting of Indigenous Māori imprisoned by the New Zealand carceral state: a national record study
Bibliographic record
Abstract
BACKGROUND: Indigenous Māori are imprisoned on a mass scale by the nation-state currently known as New Zealand, driven by racialised inequities that occur across the criminal legal system and a rapidly expanding carceral state. Lack of reliable data limits the ability to monitor and evaluate the health and disability impacts of imprisonment on Māori. We examined ethnicity data quality; specifically, potential miscounting of Māori in prison. All individuals who experienced at least one night of imprisonment between 2018 and 2021 were selected from the Department of Corrections (Corrections) data in the Stats NZ Integrated Data Infrastructure (IDI). We compared counts and proportions of Māori using two sources of ethnicity information; Corrections and IDI's core data. Within this cohort, we compared self-identified ethnicity from the 2018 Census with ethnicity recorded in Corrections data available in the IDI (via individual linkage), to assess levels of match between datasets and calculate net undercount. RESULTS: Lesser numbers of Māori were recorded in the Corrections data compared to the IDI's core data (52% versus 57% of the study cohort), a pattern observed across all age and gender groups, and amongst those sentenced and on remand. For the linked analysis, only one third (34%) of the cohort linked to the IDI central spine had self-identified ethnicity from the 2018 Census. Of this group, 46% self-identified as Māori ethnicity. When this information was compared to ethnicity information reported by Corrections for the same individuals, there was a 12% undercount of Māori in Corrections data. The net undercount of Māori was 6%, equating to at least an extra 405 Māori imprisoned than what is publicly reported by government. CONCLUSIONS: Reliable data inclusive of high-quality ethnicity data are critical for understanding and monitoring Māori health in terms of resource allocation, policy decisions, and performance of health and disability services for Māori imprisoned in NZ. Systemic undercounting of Māori in prisons is a breach of Indigenous rights to monitor and evaluate impacts of government actions and inactions for Māori. We do not accept the inevitability of prisons but whilst prisons exist, and until there are no prisons left on Māori whenua (lands), an all-of-government approach to prioritisation of high-quality ethnicity data across the criminal legal system that meets obligations to Te Tiriti o Waitangi and international human rights instruments is urgently required.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".