INTENSITY AND GAMBLING HARMS: EXPLORING BREADTH OF GAMBLING INVOLVEMENT AMONG ESPORTS BETTORS
Bibliographic record
Abstract
The esports betting market remains predominantly unregulated, although regulated sites have begun offering options to wager on these types of events. Previously, esports has been considered a niche market sector with participants differing from traditional sports and race bettors. However, as esports increasingly becomes absorbed into mainstream markets, it is important to understand the comparative similarities and differences in characteristics and behaviours between esport and traditional sports bettors. In particular, with regulators increasingly attempting to reduce use of offshore gambling sites, it is important to determine the extent to which those who bet on esports are more likely to access and engage in unregulated sites and activities. This paper classified and compared the characteristics of 501 Australians reporting participation in both sports and esports (n=160), or only in sports (n=341) bets online in the past month. Measures assessed use of online onshore and offshore sites, factors used to select gambling sites, and perceived advantages/disadvantages of using onshore compared to offshore sites. Findings suggested that the two groups were distinct in several ways; esports bettors were younger, more highly educated, had higher incomes and were represented by a greater proportion of females and individuals from Asian ethnic backgrounds. This group additionally reported starting gambling more recently and frequently overall, and expressed a preference to gamble on illegal offshore sites as opposed to domestically-licensed sites. Sports bettors were more likely to select domestic sites seeking more reliable and safe experiences, in contrast to esports bettors, who were motivated by the gambling experience, regardless of where a site was regulated. Results suggested that sports bettors perceived greater disadvantages of using offshore sites, and that this acted to deter access. In contrast, esports bettors sought a specific experience and were willing to use offshore sites. It is concluded that domestic operators need to provide a competitive online gambling environment and meet customer demands if online gamblers are to be deterred from using offshore sites.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".