Equivalent gambling warning labels are perceived differently
Bibliographic record
Abstract
BACKGROUND AND AIMS: The same information may be perceived differently, depending on how it is described. The risk information given on many gambling warning labels tends to accentuate what a gambler might expect to win, e.g. 'This game has an average percentage payout of 90%' (return-to-player), rather than what a gambler might expect to lose, e.g. 'This game keeps 10% of all money bet on average' (house-edge). We compared gamblers' perceived chances of winning and levels of warning label understanding under factually equivalent return-to-player and house-edge formats. DESIGN: Online surveys: experiment 1 was designed to test how gamblers' perceived chances of winning would vary under equivalent warning labels, and experiment 2 explored how often equivalent warning labels were correctly understood by gamblers. SETTING: United Kingdom. PARTICIPANTS: UK nationals, aged 18 years and over and with experience of virtual on-line gambling games, such as on-line roulette, were recruited from an on-line crowd-sourcing panel (experiment 1, n = 399; experiment 2, n = 407). MEASUREMENTS: The main dependent variables were a gambler's perceived chances of winning on a seven-point Likert scale (experiment 1) and a multiple-choice measure of warning label understanding (experiment 2). FINDINGS: = 19.03, P < 0.001. In experiment 2, the house-edge warning label was understood by more gamblers [66.5, 95% confidence interval (CI) = 60.0%, 73.0%] than the return-to-player warning label (45.6%, 95% CI = 38.8%, 52.4%, z = 4.22, P < 0.001). CONCLUSIONS: House-edge warning labels on electronic gambling machines and on-line casino games, which explain what a gambler might expect to lose, could help gamblers to pay greater attention to product risk and would be better understood by gamblers than equivalent return-to-player labels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".