Ignoring Versus Exploring Differences Between Groups: Effects of Salient Color‐Blindness and Multiculturalism on Intergroup Attitudes and Behavior
Bibliographic record
Abstract
Abstract Multiculturalism and color‐blindness represent distinct, and in many ways conflicting, approaches to intergroup relations. We provide a review of the research and theory guiding use of these ideologies as prejudice reduction strategies: Is it best for individuals to ignore category memberships and focus on fundamental human qualities that everyone shares, as color‐blindness would suggest? Or should people adhere to multicultural ideals, recognizing and indeed celebrating differences between groups? After describing these ideologies and their respective theoretical underpinnings, we examine their effects on attitudes, perceptions, and intergroup interaction behavior. We emphasize in particular the link from color‐blindness to self‐focus and prevention orientation and from multiculturalism to an other‐focused learning orientation. Although color‐blindness can have positive effects in the short term, the efforts that it prompts to inhibit and suppress negative responses can be taxing and difficult to sustain. Multiculturalism triggers more positive intergroup attitudes and behavior in nonconflictual circumstances, but has the opposite effect in threatening situations. Nonetheless, because it leads to a focus on learning about others in intergroup situations multiculturalism has the virtue of generally fostering greater attention and responsiveness to outgroup members.
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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 | 0.005 | 0.017 |
| 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".