Gambling behavior of ethnic Chinese and Vietnamese college students in the United States
Bibliographic record
Abstract
This study examined gambling activity and risk for gambling problems among ethnic Chinese- and Vietnamese- American college students. The Canadian Adolescent Gambling Inventory (CAGI) was administered to a stratified sample of 653 undergraduates at a public university in the northeastern United States. This sample included racial-ethnic subsamples large enough to compare gambling behavior among Chinese, Vietnamese, Other Asian, Black, Latino, and white students. Logistic regression analysis indicated that the odds of having gambled in the past three months were lower than whites only for Other Asians [odd’s ratio(OR) = .320, p < .05] and Latinos [OR = .477, p < .05] and not for Chinese, Vietnamese, or Blacks. Among students who gambled in the last three months, the odds of meeting criteria for high severity risk of problem gambling were higher for the Chinese [OR = 5.381, p < .05], Latinos [OR = 6.520, p < .05], and Blacks [OR = 6.540, p < .05] than for whites. The odds of meeting criteria for some degree of risk were higher for Vietnamese than white students [OR = 2.736, p < .05]. These findings suggest the need for future study of underlying risk factors for students of minority racial-ethnicity.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".