Gamers Who Gamble
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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 it