Perceptions of Confronters of Racist Remarks Towards Interracial Couples: The Effects of Confronter Race, Assertiveness, Explicit Bias, and Participant Race
Bibliographic record
Abstract
Previous research demonstrates that confronting prejudicial comments reduces bias towards minority groups and that perceptions of those who confront prejudicial comments differ as a function of factors such as confronter race. The current study extends on previous research examining how participants’ race, confronters’ race, assertiveness, and racial bias affect the perceptions of individuals who confront prejudice towards interracial couples on Twitter. Black and White participants throughout the United States (N=154) viewed a Twitter post from a Black-White interracial couple followed by a racist comment and a confronting comment varying by confronter race and assertiveness. Results indicated that confronters were perceived more positively when using a low assertive than a high assertive approach and were rated more negatively by Black compared to White participants. Additionally, those with more explicit biases towards the outgroup perceived the confronter more negatively. This work can inform interventions focused on increased confronting and highlights the importance of allyship.
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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 | 0.012 |
| 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) | 0.003 | 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".