Ethnicity, Gender, and the Theory of Planned Behavior: The Case of Playing the Lottery
Bibliographic record
Abstract
This study uses the theory of planned behavior (TPB) to explain why some people play the lottery, and it examines how the TPB's variables and variable relationships differ due to ethnicity, or gender, or their interaction. A telephone interview conducted in English, Cantonese, and Mandarin resulted in data on the lottery play intentions of 208 Chinese/Canadians (97 males, 111 females) and 220 British/Canadians (112 males, 108 females). When intention to play the lottery was regressed on six TPB variables, it was found that: (a) affective attitude was an important predictor for all four groups, while instrumental attitude was only important for British/Canadian males; (b) injunctive norm was an important predictor only for Chinese/Canadian males, while descriptive norm was an important predictor only for British/Canadian males; (c) controllability was an important predictor only for Chinese/Canadian females, with a negative coefficient suggesting secondary control; and (d) self-efficacy was not an important predictor for any of the groups. A follow-up mail questionnaire provided additional data on the self-reported lottery play behavior of 100 Chinese/Canadians (51 males, 49 females) and 115 British/Canadians (57 males, 58 females) 30 days after the initial telephone interview was conducted. When lottery play behavior was regressed on self-efficacy, controllability, and intention, intention was found to be an important predictor for all four groups. These findings are discussed in light of recent research on the TPB, leisure and gambling, and ethnicity and gender.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".