Between-session chasing of losses and wins in an online eCasino
Bibliographic record
Abstract
Continued gambling despite negative consequences, commonly known as ‘chasing’, is a defining feature of disordered gambling. Yet chasing is also a complex and multi-faceted behavioural phenotype; for example, gamblers may chase winning outcomes as well as losses. This study characterized between-session chasing behavior in a large naturalistic dataset of online gambling data, comprising 1,909,681 eCasino sessions played by 15,544 individuals on PlayNow.com, the provincial online gambling platform in British Columbia, Canada. Analyses distinguished sessions on slot machines (as the reference category), blackjack, roulette, video poker, probability games, or mixed sessions. Overall, gamblers returned more slowly after losing sessions, and more quickly after winning sessions, across most product categories. For every standard deviation increase in the prior session net loss, slot machine gamblers took 8.59% longer to return to the website (b = 0.08, p < .001). For every standard deviation increase in the prior session net win, slot machine gamblers returned 6.68% faster (b = -0.07, p < .001). Loss chasing intensities in blackjack, probability, video poker, and mixed sessions did not differ significantly from slot machine sessions, but roulette was associated with a shorter interval to return (b = -0.13, p < .001). Similarly, win chasing intensities across blackjack, probability games, and video poker did not differ significantly from slot machine sessions, but roulette (b = -0.08, p < .001) and mixed (b = -0.02, p = 0.009) sessions were associated with shorter intervals to return. Average behavioural patterns provide limited evidence for loss chasing in the interval between sessions, but gamblers return faster after larger wins. Although slot machines are commonly considered as high-risk gambling products, in our analyses online roulette was associated with the greatest chasing intensities.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".