Bibliographic record
Abstract
Abstract This chapter reviews two strategies for managing alcohol deployed in some Aboriginal communities: community-owned liquor outlets, usually in the form of licensed clubs, and liquor permit systems that authorise approved individuals to import and consume liquor in communities where doing either is otherwise not allowed under local restrictions. The rationale underlying community-owned outlets is that they retain the revenue derived from drinking by community members in the community, foster a culture of moderation and deter illicit importation of liquor (i.e. ‘grogrunning’). Historically, most community-owned outlets have failed to achieve either the second or third of these objectives, but rather have become centres for heavy drinking and associated harms. Some community-owned outlets, however, have succeeded in fostering moderate drinking, and the chapter outlines factors conducive to their doing so. The use of individual liquor permit systems today is confined to some remote communities in the Northern Territory, Australia, and some Inuit communitiesin Nunavut, Canada. Evidence of their impact is sparse but suggests that liquor permit systems can enhance community management of alcohol provided three conditions are met: permit committees and others responsible for administering permit systems are adequately supported and resourced; effective controls are in place to deal with illegal supply of alcohol, and the rules and procedures that constitute the permit system enjoy legitimacy in the eyes of the community.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".