Measuring problem gambling in Indigenous communities: An Australian response to the research dilemmas
Bibliographic record
Abstract
\n\t\t\t\t\tThis paper examines evidence relating to harmful consequences of gambling in the Australian Indigenous population and highlights the failure of research to date to define problem gambling from Indigenous perspectives or to tailor research processes to accommodate the cultural beliefs and experiences of Indigenous groups. It advocates for the development of a unique set of measures to assess the function of problem gambling aspects, negative impacts, trends, risks and protective factors. This would be informed by more recent qualitative studies into gambling that are specific to Indigenous communities. Additionally, this paper argues the need to adapt and validate a commonly applied assessment tool, such as the Canadian Problem Gambling Index, to monitor prevalence of problem gambling over time. Targeted research into Indigenous people's experiences of gambling will facilitate the development of culturally based responses and interventions into problem gambling.\n\t\t\t\t
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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 | 0.084 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".