The interplay of individual differences, norms, and group identification in predicting prejudiced behavior in online video game interactions
Why this work is in the frame
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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.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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