Gamers Who Gamble
Bibliographic record
Abstract
During recent years, while electronic sports (esports) has increasingly become a positive mainstream cultural phenomenon, it also may have several socio-economic implications, such as the growth of esports betting. Much like betting in sport, betting on esports has become a prominent form of gambling. However, there is still a paucity of knowledge on the demographic characteristics of this gambling cohort, particularly in regard to its relationship to video game play and spectatorship. In the present study, past-year video gamers (N = 1368) completed an online survey. Survey questions inquired about their esports event spectating, video game play, and esports betting behaviours, as well as general demographic questions. Video gamers who bet on esports were a distinct cohort from their counterparts: younger, more likely to be male, lower frequency of video game play, higher frequency of esports spectatorship, and more likely to watch esports in a social setting (e.g., with others). By providing a background on gamers’ behaviours this work contributes to the growing body of research into the dynamic profile of esports play, spectatorship, and gambling. Findings are reflective of the growing interrelation of gambling and gaming behaviours, a subject garnering increasing attention from governments, regulatory agencies, public health specialists and clinicians, and the related industries themselves.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".