Investigating preferences for gaming machine features in problem and non-problem gamblers using a consumer choice methodology
Bibliographic record
Abstract
Conjoint analysis is a specialized statistical technique that is used widely in studies of consumer psychology and marketing to determine the relative importance of specific product characteristics in customer choice. In this form of analysis, a finite and pre-determined series of n product characteristics is used to generate sets of product configurations that are then ranked by respondents. Using these rankings, it is possible to generate individualized profiles of the relative proportion of choice governed by each characteristic in the form of 'part-worth' estimates. In this study, a sample of 41 regular EGM gamblers (24 moderate risk and problem gamblers and 17 non-problem gamblers) as classified by the Canadian Problem Gambling Index (CPGI) were asked to rank their preferences for commercially available gaming machines, that varied in terms of the maximum prize, credit denomination, maximum lines, and the availability of free-spin or bonus features. This paper summarises the potential contribution of conjoint analysis to the study of gambling and provides a comprehensive summary of the similarities and differences observed between the choice profiles of problem and non-problem gamblers. The implications of these findings for regulation and responsible gambling are 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".