Bibliographic record
Abstract
While esports betting is becoming a big business worldwide, scientific studies primarily focus on an Anglo-Saxon perspective to understand the phenomenon and its users. In this cross-sectional online survey study, we therefore investigated the esports betting profile and motivations of Dutch-speaking, European adults playing or watching video games. We conceptually replicated the American esports bettor profile to the study of Abarbanel, Macey, Hamari, and Melton (2020) in a European context, assessed the robustness of our replication with gambling-behavioral variables, and used an adapted version of the Modified Gambling Motivations Scale to unravel EU-esports bettors’ most salient betting motives. Our results reveal that the America-oriented esports bettor profile cannot be replicated in our European sample, making us question whether socio-demographics, such as gender and age, and gameplay-related variables such as video gaming frequency are robust correlates of esports betting participation. Instead, these findings suggest that involvement in esports spectatorship and the breadth and depth of participation in other gambling activities deliver the most accurate predictions, and that esports bettors are mostly driven by intrinsically- and extrinsically-oriented motives such as excitement, intellectual challenge, and monetary gain. This study may form a starting point for future research into the measurement, targeting, and regulation of esports, and gambling-related ‘at risk’ behaviors and audiences, within the European Union.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".