Pareto effects in the eCasino: differences across online products and links with self-exclusion
Bibliographic record
Abstract
Consumption of many goods adheres to the Pareto rule (or 80:20 law) that the 20% most engaged consumers generate 80% of revenue. Using a dataset from the eCasino section of the provincial online gambling platform in British Columbia, Canada, we recently observed that Pareto values exceeded the conventional 80:20 ratio, with the top 20% generating ~92% of bets and ~90% of revenue (Deng, Lesch & Clark, 2021 Addictive Behaviors 120: 106968). In this presentation, we will examine how these effects vary across different gambling product types within the eCasino: online slot machine games, table games, video poker, and other probability games. As a marker of likely gambling harm, we test rates of enrolment in Voluntary Self-Exclusion (VSE). The dataset comprises 30,920 account holders who placed at least one bet on the platform in 2014-2015, comprising over half a billion individual bets. Across the four product types, Pareto values indicated greatest concentration for video poker (98%) and lowest for slot machines (89%). We also observe higher levels of VSE in the top 20% compared to the remaining 80% (13% vs 7%) with some differences across products.\nStatement of Importance: As online gambling expands, behavioural tracking offers a promising technique for detecting risk, but limited work has examined behavioural markers across different online gambling products. We examine concentrations of consumption using the Pareto rule across 4 different product types in an online casino, and associations with gambling self-exclusion.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".