Normalization of toxicity scale: A measure of toxicity normalization in the online gaming community
Bibliographic record
Abstract
Toxic behavior in video games is a complex and pervasive problem that affects all aspects of video games, including their development. Its effect on player wellbeing, enjoyment, and retention reverberates throughout the industry. Research has revealed that toxic behavior in games is becoming normalized at an alarming rate. The purpose of this study was to create and validate a scale to measure the perceived normalization of toxic behavior using six constructs taken from three behavioral theories: Past Victimization Experiences and Self-Efficacy from Social Cognitive Theory; Subjective Norms, Attitudes, and Behavioral Control from Theory of Planned Behavior; and Toxic Disinhibition from Online Disinhibition Effect. Based on these constructs, an initial 25-item pool was generated. Four additional scales were added to establish validity, the Toronto Empathy Scale, the Buss-Perry Aggression Questionnaire, the Marlowe-Crowne Social Desirability Scale, and the Moral Disengagement in Sports Scale. Exploratory and confirmatory factor analyses were used, which required the resulting dataset to be split into two groups for analysis with each group having 229 participants. Exploratory factor analyses (n=229) pared the original 25-item scale to 16 items across 3 distinct factors: 8 items represented Toxicity Normalization, 4 items represented Past Victimization, and 4 items represented Impulsive Toxicity. Confirmatory factor analysis (n=229) was performed utilizing the model obtained from exploratory factor analysis. Results of the analyses indicate acceptable reliability and evidence of validity, but more research is needed for validation. Implications of the current study for researchers and practitioners are presented. Lastly, future research and recommendations for improving the scale’s quality are discussed.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".