Killing with Kindness: The Influence of Spectator Presence and Tone on Performance in Competitive Video Games
Bibliographic record
Abstract
When playing competitive video games, players are subject to various pressures that may jeopardise performance outcomes. Prominent among these pressures is spectatorship: that is, the expectation that one maintains high performance under scrutiny by teammates, audience members, or others. In this work, we explore the influence of spectatorship—and the tonality of response—on player performance in a competitive racing video game. We report results from a mixed-methods laboratory experiment (n=85), counter-intuitively finding that supportive spectatorship is more detrimental to performance than critical spectatorship. We contend that performance may be undermined by expectation threat, nervous activation, and presentational concerns, as well as participant trait self-consciousness. This work makes a novel contribution to the empirical investigation of spectatorship influence on video game performance—positing implications for both game developers and high-pressure gaming spaces, such as esports.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".