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Record W3134361780 · doi:10.1080/14459795.2021.1883708

Attitudes toward gambling in young people: a cross-national study of Australia, Canada, Croatia and Israel

2021· article· en· W3134361780 on OpenAlexaffabout
Paul Delfabbro, Belle Gavriel‐Fried, Neven Ricijaš, Dora Dodig, Jeff Derevensky

Bibliographic record

VenueInternational Gambling Studies · 2021
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsPerceptionDemographic economicsPsychologyPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Public attitudes toward gambling have important implications for people’s engagement in the activity and receptivity to regulatory reforms. Such views are likely, however, to be influenced by variations in market conditions, perceptions of regulations and personal exposure to gambling. This article examines whether differences in gambling attitudes are related to differences in the perceived social, cognitive, and physical accessibility of gambling in four countries (Australia, Israel, Croatia and Canada). These countries were selected because they cover a range of gambling regulations, from established liberalized markets (Australia, Canada), to a recently liberalized market (Croatia) to a relatively restricted market (Israel). University student respondents (n = 1787, aged 18–30) were surveyed in these four countries to control for educational differences. Within- gender analyses controlled for differences in gender profile across countries. More positive attitudes were associated with greater social accessibility and more stringent regulations. Australian and Canadian respondents reported more positive attitudes toward gambling and regulations. Israeli respondents reported less positive attitudes and exposure to problem gambling. Croatian respondents reported more positive attitudes, but considered gambling to be poorly regulated and overly available. Overall, attitudes were related to perceptions of regulation as well as the duration of exposure to liberalized markets.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.237
GPT teacher head0.499
Teacher spread0.261 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2021
Admission routes2
Has abstractyes

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