Esports consumer perspectives on match-fixing: implications for gambling awareness and game integrity
Bibliographic record
Abstract
This article examines consumer perspectives on match-fixing in esports – professionalized competitive video game play – and the implications of these perspectives for understanding game and gambling integrity. The relationship between match-fixing, game integrity and gambling is a close one, as gambling markets are reliant on strong game integrity, but has not yet been studied in detail in the context of esports. Drawing on extensive qualitative data collected from esports fans around the world, this article examines perceptions of gambling awareness, integrity and esports gambling to assess esports consumers’ awareness of and attitudes towards gambling-related match-fixing. Results indicate that esports viewers are not deeply concerned by match-fixing. In addition, spectators typically view gambling as a cause of corruption among competitors, but also understand and accept some elements of the practice. Further, spectators tend to rely on rules to determine their assessment of what is ‘wrong’, rather than assessments based on ethics, and are often willing to forgive infractions through a range of reasons and justifications. We propose a need for education among esports spectators, extending existing anti-cheating programmes beyond just athletes to include the broader esports community.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".