Inter- and Intra-Gender Similarities and Differences in Motivations for Casino Gambling
Bibliographic record
Abstract
The two objectives of this study were to examine if motivations for casino gambling vary by gender and, based on motivations for casino gambling, to ascertain different types of male and female gamblers. To accomplish these objectives, five casino motivation scales were developed. Nine hundred male and female casino patrons living in two major Canadian metropolitan areas completed a telephone questionnaire. Male study participants rated risk-taking/gambling as a rush and learning/cognitive self-classification as being more important than did female participants. Two types of male casino gamblers existed: men who gave primacy to risk-taking/gambling as a rush and emotional self-classification, and men who gave primacy to communing. Three types of female casino gamblers existed: women who gave primacy to emotional self-classification and escaping everyday problems, women who gave primacy to communing and emotional self-classification, and women who gave primacy to communing alone. Gender theory was used to explain these findings, and study limitations and future research recommendations also were discussed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".