More similarity if different, more difference if similar: Assimilation, colorblindness, multiculturalism, polyculturalism, and generalized and specific negative intergroup bias
Bibliographic record
Abstract
The creation of a social climate where all ethnic groups can harmoniously coexist is a central challenge for many countries today. Should we emphasize similarities and common ground or, conversely, recognize that there are important differences between groups? The current study examined relations between diversity ideologies (assimilation, colorblindness, multiculturalism, polyculturalism) and generalized and specific intergroup bias (against Chechens, Belarusians, Uzbeks, Chinese, and Jews and Muslims) among ethnic Russians (N = 701). In Study 1, colorblindness (ignoring differences) and polyculturalism (emphasizing interconnectivity) were associated with lower generalized intergroup bias and lower bias against Chechens, Uzbeks, and Chinese, but not Belarusians. Bias against Belarusians was lower among those who endorsed multiculturalism (emphasizing differences). In Study 2, multiculturalism was associated with higher implicit bias when the target was a Chechen but in general more proximal variables (positive or negative contact experience and perceived group similarity) were more robust predictors of intergroup bias than diversity ideologies. In Study 3, colorblindness and polyculturalism were related to lower levels of fearful attitudes against Muslims. Colorblindness was also associated with lower levels of Antisemitism in contrast to multiculturalism that had an opposite association. We place these results in the context of cultural distance and existing cultural stereotypes about different groups among the majority of Russians. The strengths and weaknesses of each diversity ideology for the mainstream cultural group are discussed. The results of the current study suggest that the most fruitful strategy for mainstream cultural groups for maintaining harmonious intergroup relations in diverse societies might be that of optimal distinctiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".