MétaCan
Menu
Back to cohort
Record W2157468193 · doi:10.4309/jgi.2011.25.3

Mahjong and Problem Gambling in Sydney: An Exploratory Study with Chinese Australians

2011· article· en· W2157468193 on OpenAlexvenueaboutno aff
Wu Yi Zheng, Michael Walker, Alex Blaszczynski

Bibliographic record

VenueJournal of Gambling Issues · 2011
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMainstreamChinese communityPsychologySample (material)AdvertisingChinese peopleChinaSocial psychologyDemographySociologyPolitical scienceLawBusiness

Abstract

fetched live from OpenAlex

Gambling is accepted as an integral part of Chinese cultural heritage. Epidemiological and clinical studies indicate that problem gambling rates among Chinese community members residing in Western countries are substantially higher (2.1-2.9%) compared with those reported for mainstream populations (0.5-1.7%). However, these studies failed to differentiate culturally specific forms of gambling and their association with problem gambling within Chinese samples. Thus, it is not possible to determine if, or what proportion of, Chinese problem gamblers exhibit a propensity to experience problems with culturally specific, as opposed to mainstream, forms of gambling. Mahjong, a popular game deeply entrenched in Chinese tradition, is played among peers and family members. In a recent study conducted by Zheng, Walker, and Blaszczynski (2008), high rates of Mahjong-associated problem gambling were found in a sample of Chinese international students attending language schools and universities in Sydney, Australia. The aim of the current study was to explore the extent of Mahjong-associated problem gambling in a broader community sample of Chinese Australians. Results showed that in a sample of 229 respondents, males and those 35 years or older were more likely to gamble on Mahjong and that 3.1% met the Canadian Problem Gambling Severity Index criteria for Mahjong problem gambling.

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)0.000
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 score0.038
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.322
GPT teacher head0.450
Teacher spread0.128 · 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

Citations3
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Gambling IssuesSame topicGambling Behavior and TreatmentsFrench-language works237,207