Don’t You Know That You’re Toxic: Normalization of Toxicity in Online Gaming
Bibliographic record
Abstract
Video game toxicity, endemic to online play, represents a pervasive and complex problem. Antisocial behaviours in online play directly harm player wellbeing, enjoyment, and retention—but research has also revealed that some players normalize toxicity as an inextricable and acceptable element of the competitive video game experience. In this work, we explore perceptions of toxicity and how they are predicted by player traits, demonstrating that participants reporting a higher tendency towards Conduct Reconstrual, Distorting Consequences, Dehumanization, and Toxic Online Disinhibition perceive online game interactions as less toxic. Through a thematic analysis on willingness to report, we also demonstrate that players abstain from reporting toxic content because they view it as acceptable, typical of games, as banter, or as not their concern. We propose that these traits and themes represent contributing factors to the cyclical normalization of toxicity. These findings further highlight the multifaceted nature of toxicity in online video games.
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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 | 0.003 | 0.014 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".