Expecting racial outgroups to view “us” as biased: A social projection explanation of Whites’ bias meta-stereotypes
Bibliographic record
Abstract
Meta-stereotypes, the stereotypes believed to be held about one’s ingroup by an outgroup, represent barriers to positive intergroup contact. Little is known, however, about factors accounting for meta-stereotypes. Although previous researchers have speculated on conceptual overlap between social projection (perceiving one’s personal attitudes to be commonly held) and meta-stereotypes, these constructs are typically studied separately. We propose the notion that meta-stereotypes can be explained by social projection processes. We examined Whites’ “bias meta-stereotypes” (perceptions that Blacks consider Whites biased) across two studies. Participants projected personal biases onto both their ingroup (Whites) and outgroup (Blacks); in turn, both ingroup and outgroup bias perceptions uniquely predicted bias meta-stereotypes. Overall, the positive relation between personal bias perceptions and bias meta-stereotypes was fully mediated (i.e., explained) by heightened perceptions of ingroup (White) and outgroup (Black) bias. Overall, there is considerable value in integrating basic social projection within intergroup domains, particularly with regard to meta-stereotyping.
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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 | 0.006 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".