House-edge information yields lower subjective chances of winning than equivalent return-to-player percentages: New evidence from support forum participants
Bibliographic record
Abstract
Information messages that communicate the average cost of play are a helpful consumer protection tool in gambling. In Australia and the United Kingdom, cost of play information is typically communicated via the “return-to-player” statistic, e.g., “This game has an average percentage payout of 90%.” Through a sample recruited through a gambling support forum (n = 49), this paper reports how house-edge information (e.g., “This game keeps 10% of all money bet on average”) is associated with lower perceived chances of winning, as opposed to equivalent return-to-player information. Accordingly, this study also extends the literature on optimal gambling messaging to a group of support forum users.RésuméLes messages destinés à renseigner les joueurs sur le coût moyen des activités de jeux de hasard constituent une mesure de protection du consommateur utile. En Australie et au Royaume-Uni, cette information est habituellement transmise sous forme de statistique précisant le « taux de retour », par exemple : « Ce jeu a un taux de retour de 90 %. » Cette étude montre, sur la base d’un échantillon recruté au sein d’un forum de soutien aux joueurs (n=49), que les messages axés sur la marge de profit des maisons de jeu (par ex. « La maison conserve en moyenne 10 % des sommes misées ») ont une incidence négative sur la perception des chances de gagner, contrairement aux messages sur le taux de retour. Nos conclusions ajoutent aux connaissances sur la nature des messages à adresser aux membres des groupes de soutien aux joueurs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".