Does one shoe fit all? Impacts of gambling among four ethnic groups in New Zealand
Bibliographic record
Abstract
The aim of the current study is to examine the impacts of gambling among four different ethnic groups within New Zealand (i.e., Maori, Pakeha, Pacific peoples, and Chinese and Korean peoples). Four thousand and sixty-eight Pakeha, 1,162 Maori, 1,031 Pacific people, and 984 Chinese and Korean people took part in a telephone interview that assessed their gambling participation and their quality of life. Results showed a number of differences between ethnic groups. For the Maori and Pacific samples, there were significant associations between gambling participation (especially time spent on electronic gaming machines) and lower ratings in a number of life domains. In contrast to the findings for the Maori and Pacific peoples, which showed predominantly negative associations between gambling modes and people's self ratings of their domains of life, the findings for Pakeha and for Chinese and Korean peoples were more mixed and the associations predominantly positive.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".