European Americans’ Intentions to Confront Racial Bias: Considering Who, What (Kind), and Why
Bibliographic record
Abstract
Confrontation research has primarily focused on what drives individuals’ intentions to confront strangers who express prejudicial attitudes (i.e., interpersonal bias; for reviews see Ashburn-Nardo & Karim, 2019; Kawakami et al., 2019). However, bias manifests in multiple forms, including biased policies and institutional practices (i.e., structural bias) or bias perpetrated by close others (e.g., friends), and little is known about what factors impede (or facilitate) intentions to confront these different manifestations of bias. Across three experiments, European Americans reported wanting to confront instances of structural racial bias more than interpersonal racial bias. This was driven by perceptions that the examples of structural bias were more harmful and that confronting would be more effective in changing the perpetrator’s behavior, compared with examples of interpersonal bias. Additionally, participants expressed greater intentions to confront friends over strangers (Studies 1-2), due to participants’ perceptions that they personally would be effective confronters and that friends would be more receptive. This work provides insight into people’s intentions to confront varying manifestations of bias, namely biased structures and close others.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 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".