Bibliographic record
Abstract
In 2008, I lodged a claim with the Waitangi Tribunal in regard to problem gambling and its negative impacts on Māori people. The Tribunal is tasked with hearing grievances related to Te Tiriti o Waitangi (The Treaty of Waitangi) signed in 1840 between Māori and the British Crown. It is a historical claim focused on the lack of adequate protection of taiohi Māori (young people of Māori descent) and the intergenerational harm caused by problem gambling among their whānau, hapū, iwi (extended families and relatives) and urban Māori communities. However, this begs the question how can a Treaty claim improve the health outcomes of a generation of taiohi Māori who have been exposed to commercial gambling and its aggressive and targeted expansion and marketing? This paper frames the WAI-1909 claim as a Kaupapa Māori (Māori research approach) derived from the research of three wahine toa (warrior women) supporting the claim; and refers to epistemological standpoints of Māori women working in the gambling research space. I demonstrate how the gambling claim challenges the New Zealand government to honour the promises in the articles of Te Tiriti o Waitangi and to protect the rights of its citizens, especially taiohi Māori. The WAI-1909 gambling claim concludes that whilst the New Zealand Gambling Act (2003) includes a public health approach to problem gambling, it has not adequately addressed the rights of tangata whenua (Māori, the first people of Aotearoa/New Zealand) under Te Tiriti o Waitangi.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".