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Record W2982653979 · doi:10.1111/dar.12994

Do individual liquor permit systems help Indigenous communities to manage alcohol?

2019· article· en· W2982653979 on OpenAlexaboutno aff
Peter d’Abbs, Ian Crundall

Bibliographic record

VenueDrug and Alcohol Review · 2019
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
FundersMenzies School of Health Research
KeywordsIndigenousBusinessEnforcementPurchasingConsumption (sociology)Political scienceLawMarketingEcologySociology

Abstract

fetched live from OpenAlex

INTRODUCTION AND AIMS: Liquor permits were once used throughout Scandinavia and North America for managing alcohol, but largely disappeared in the late 20th century. Today, they are used in some Indigenous communities in Nunavut, Canada and the Northern Territory, Australia. This paper examines the extent to which liquor permits: (i) contribute to reducing alcohol-related harms in Indigenous communities; and (ii) offer a viable mechanism for managing alcohol in Indigenous communities. DESIGN AND METHODS: The study draws on published and unpublished international literature on liquor permit systems in Indigenous communities, and on field visits to northern territory (NT) communities. RESULTS: Apart from one anecdotal report, the study found no evidence that liquor permit systems in Nunavut communities have reduced alcohol-related problems. In the NT, they have reduced alcohol-related harms in some communities. However, management of liquor permit systems generates significant administrative demands in communities. DISCUSSION AND CONCLUSIONS: Effectiveness of liquor permit systems is a product of five factors: permits themselves; agencies and procedures for issuing and managing permits; agencies and procedures for supplying liquor; enforcement of permit conditions, and the presence of other agencies-legal and illegal-affecting supply and consumption of liquor. Liquor permits continue to be valued by some Indigenous communities for managing alcohol. This study suggests that they can do so provided: (i) agencies administering permits have adequate support; (ii) controls over non-legal purchasing and consumption of liquor are effective, and (iii) the permit system is viewed in the community as legitimate, equitable and transparent.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.004

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.

Opus teacher head0.080
GPT teacher head0.393
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2019
Admission routes1
Has abstractyes

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