Scratch Card Near-Miss Outcomes Increase the Urge to Gamble, but Do Not Impact Further Gambling Behaviour: A Pre-registered Replication and Extension
Bibliographic record
Abstract
Scratch card near-misses, outcomes in which two out of three required jackpot symbols are uncovered, have been shown to erroneously increase the urge to continue gambling. It remains unknown if and how these outcomes influence further gambling behaviour. Previous studies examining the influence of near-misses on purchasing behaviour offered a low-stakes gamble to participants after experiencing a near-miss or a regular loss. We sought to investigate the influence of these outcomes on scratch card purchasing behaviour with a stronger test of participants' gambling behavior by having them either "cash out" or risk all of their winnings to purchase another card. Additionally, we sought to test an original hypothesis that endorsement of the illusion of control might influence the decision to purchase additional scratch cards. We pre-registered our hypotheses, sample size, and data analysis plan. 138 subjects experienced two custom-made scratch card games that included a win on the first card (for all participants) and either a regular loss or a near-miss in the final outcome position on the second card (between-subjects manipulation). Although near-miss outcomes increased the urge to continue gambling relative to regular losses, no differences in the rates of purchasing were found between the conditions. Additionally, no support for our hypotheses concerning the influence of the illusion of control in near-miss outcomes was found. These results are discussed in terms of previous studies on scratch card gambling behaviour and subjective reactivity.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".