Cross-Cultural Studies Into Gambling Consumption Behavior: Eyeing Eye-Tracking Measures
Bibliographic record
Abstract
“Every man's ability may be strengthened or increased by culture”—John Abbot (Textappeal, 2017).\nWhile data aggregated from gambling operators have shown cross-cultural differences in the behavior of their customers (CasinoBeats, 2020), in recent years research into gambling consumption has been strengthened and enriched by studies uncovering the roles of culture in shaping gambling relevant phenomena (Oei et al., 2019). These studies were commonly based on data collected through survey questionnaires (e.g., Rinker et al., 2016; Calado et al., 2020) or interviews (e.g., Radermacher et al., 2016; Egerer and Marionneau, 2019). In the present opinion paper, it is argued that eye-tracking measurements should also be adopted in cross-cultural gambling research, particularly given the systematic differences in visual attentional patterns that potentially exist among gambling product consumers from different cultures.
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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 | 0.004 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".