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Record W2604340529 · doi:10.1080/09687637.2017.1306023

Investigating displacement effects as a result of the Sydney, NSW alcohol lockout legislation

2017· article· en· W2604340529 on OpenAlexfundno aff
Caitlin Hughes, Alexander Shou Weedon-Newstead

Bibliographic record

VenueDrugs Education Prevention and Policy · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
FundersMcGill University
KeywordsAmenityLegislationPrecinctEntertainmentParliamentAlcohol consumptionGeographyDisplacement (psychology)BusinessSocioeconomicsPolitical scienceAlcoholLawSociologyPsychologyArchaeology

Abstract

fetched live from OpenAlex

Aims: On 30 January 2014, in efforts to reduce alcohol-related violence, the New South Wales Parliament adopted alcohol lockout legislation targeting a geographically defined area: the Sydney entertainment precinct. This study used qualitative methods to explore with stakeholders of two areas (Kings Cross/Potts Point and Newtown) whether there was any evidence of displacement of alcohol-related problems from inside to outside the lockout zone.Methods: Four focus groups were conducted in mid-2015 with residents and patrons: two in Kings Cross/Potts Point (where the measures were implemented) and two in Newtown (an alternative entertainment precinct exempt from the measures). Each explored experiences pre and post lockouts and any perceived changes in the number of people in the area, level of disorderly conduct, patterns of drug and alcohol consumption, public amenity and public safety.Findings: Stakeholders in Kings Cross/Potts Point reported many improvements since the reforms: including reduced patron numbers, less waste and improved public amenity. However, stakeholders from Newtown reported the opposite: increased patron numbers, reduced public amenity and reduced public safety.Conclusions: This provides tentative evidence that even if the Sydney lockouts have reduced alcohol-related violence there may have been a partial displacement of alcohol-related problems to outside the lockout zone.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.471
Teacher spread0.430 · 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 designObservational
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

Citations13
Published2017
Admission routes1
Has abstractyes

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