Aggressive confrontation shapes perceptions and attitudes toward racist content online
Bibliographic record
Abstract
With more people using social media on a daily basis and the prevalence of racial discrimination online, it becomes imperative to understand what factors impact minority individuals’ perceptions of these transgressions in an online context. Confrontation to discrimination in the form of comments on social media may meaningfully shape perceptions of racism online. Across three studies, we examine how confrontation type (aggressive vs. passive) and confronter group membership (ingroup vs. outgroup) influence Asian Americans’ perceptions of online prejudice and attitudes towards the confronters. In Study 1, we find that aggressive confrontations alter perceptions of a racist online post to be more offensive as compared to passive confrontations. In Study 2, these findings extend to participants’ likelihood to report the content as offensive. Lastly, in Study 3, we find that aggressive confronters are evaluated more positively than passive confronters. These findings have important implications for understanding racial discrimination in an online context by demonstrating the impact of confrontation type on minority individuals’ perceptions and behaviors.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".