The interplay of individual differences, norms, and group identification in predicting prejudiced behavior in online video game interactions
Bibliographic record
Abstract
Abstract Despite the increasing popularity of video games and the diversity of people who play, prejudice remains common in online gaming. In the current study, we use structural equation modeling to test the role of social norms, individual differences, and gamer identification as predictors of how likely someone is to report engaging in prejudiced behavior while playing online video games. We also test the relative importance of these predictors to assess how likely people are to confront prejudice when it occurs in online video games. Participants (N = 384) completed a series of questionnaires to assess their attitudes and perceptions of online gaming norms, as well as to report their own prejudiced and confrontation behavior in video games. We found that both social norms and individual differences are significant predictors of behavior in online gaming. The more normative people report prejudice to be, the more they report making prejudiced comments. Similarly, the more normative confrontation of prejudice is reported to be, the more likely people are to report confronting prejudice. The more people endorsed generally prejudiced attitudes, the more likely they were to report making prejudiced remakes in online gaming and the less likely they were to report confronting prejudiced remarks. These results provide a foundation to inform interventions to reduce prejudice in gaming and indicate that both individual differences and norms are important to consider when designing interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".