Enhanced Esports: Community Perspectives on Performance Enhancers in Competitive Gaming
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
This work explores perceptions of performance enhancer usage in esports. Specifically, we explored the perception of: food and food supplements; non-medical use of prescription drugs; drugs with some social acceptance (e.g. alcohol, nicotine, cannabis); drugs with lower social acceptance (e.g., psychedelics, opioids); and non-invasive brain stimulation (e.g. transcranial direct current stimulation). A mixed-methods approach was used to triangulate findings around three data sets, including both prompted and unprompted online forum comments, as well as survey data. The studies evidence that players are willing to use or are already using enhancers to increase their in-game performance, and that players are generally concerned about the use of enhancers in professional esports contexts. Furthermore, the community perceives that a substantial number of e-athletes use enhancers. The core contribution of this work is a comprehensive investigation into perspectives of esports performance enhancement, which highlights the urgent need for further research, as well as regulation by esports leagues.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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