Fighting fair: community perspectives on the fairness of performance enhancement in esports
Bibliographic record
Abstract
Aims: This study aims to explore community perspectives on enhancer usage in competitive gaming and esports, focusing on the perception of fairness and concerns about various potential performance enhancers. Methods: We conducted both qualitative and quantitative surveys to understand the competitive gaming community's opinions on different types of performance enhancers and their potential impact on esports. A thematic analysis was performed to identify key themes in how players rationalize their opinions. Conclusions: The gaming community differentiates between potential performance enhancers based on how problematic they are for the esports scene, with the most concern surrounding hard drugs, pharmaceuticals, and brain stimulation interventions. Participants who are more invested in competitive gaming tend to be more sceptical of enhancers and express greater concerns. Four themes were identified in the thematic analysis: (1) risk, (2) morality, (3) enhancer effects, and (4) regulation. To increase acceptance and perceived legitimacy in decision-making, it is recommended that regulators engage a variety of stakeholders in transparent decision-making processes when forming tournament rules and regulations. This will help address the fragmented regulatory landscape and prevent potential differences in the perception of tournament winners based on the governing body supervising the competition.
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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 | 0.025 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".