Built-in bad luck: Evidence of near-miss outcomes by design in scratch cards
Bibliographic record
Abstract
Scratch cards are a pervasive form of gambling in the Canadian marketplace. Despite their widespread appeal, we are only beginning to understand the influence of their structural characteristics on the player. The most widely studied of these characteristics is the near-miss, a game outcome in which the player gets two of the three needed symbols to win a jackpot prize. Although other authors have noted the existence of these outcomes in scratch cards, no systematic investigation has been undertaken to understand their occurrence in these games. We present the results of an analysis to determine the frequency of these outcomes using two samples (sample A, n = 41; sample B, n = 61) of a popular scratch card game available in Ontario, Canada. Our results suggest that certain scratch card games may be designed to include more pairs of jackpot symbols (i.e., more near-miss outcomes) than any other symbol pair. In the game that we analyzed, the top prize symbol occurred more often than any other symbol and appeared to be manipulated to appear in clusters of two, creating many near-miss outcomes to the jackpot prize. This work has strong implications for the study of gambling behaviour, responsible gambling strategies, as well as for the scientific investigation of scratch card games. Les cartes à gratter sont une forme très répandue de jeux sur le marché canadien. Malgré leur grand attrait, nous commençons à comprendre l’influence de leurs caractéristiques structurelles sur le joueur. La caractéristique la plus étudiée parmi elles est un résultat s'approchant du résultat gagnant; le joueur obtient deux des trois symboles nécessaires pour gagner un gros lot. Bien que d’autres auteurs aient noté l’existence de ce genre de résultats dans des cartes à gratter, aucune enquête systématique n’a été entreprise pour comprendre leur occurrence dans ces jeux. Nous présentons les résultats d’une analyse pour déterminer la fréquence de ces résultats en utilisant deux échantillons (échantillon A, n = 41; échantillon B, n = 61) d’un jeu de cartes à gratter populaire, vendu en Ontario, au Canada. Selon nos résultats, certains jeux de cartes à gratter peuvent être conçus pour inclure plus de paires de symboles pour le gros lot (c’est-à-dire des résultats plus proches) que n’importe quelle autre paire de symboles. Dans le jeu que nous avons analysé, le symbole du prix le plus élevé était présent plus souvent que tout autre symbole et semblait être manipulé pour apparaître en grappes de deux, créant de nombreux résultats proches du résultat gagnant. Ce travail a de fortes répercussions pour l’étude du comportement du jeu, du jeu et des stratégies responsables, de même que pour l’étude scientifique des jeux de cartes à gratter.
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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 | 0.008 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".