Towards an Integration of Models of Discrimination of Immigrants: from Ultimate (Functional) to Proximate (Sociofunctional) Explanations
Bibliographic record
Abstract
We integrated models of discrimination of immigrants by combining established approaches to prejudice and discrimination towards immigrants ( proximate explanations ) using assumptions of Evolutionary-Coalitional Theory ( ultimate explanations ). Based on this perspective, right-wing authoritarianism (RWA), social dominance orientation (SDO), and multicultural ideology (MCI) were considered as sociofunctional motives for attitudes towards immigrants. We examined relationships between individual differences in beliefs about the social world (dangerous worldview and competitive worldview) as more distal antecedents, and RWA, SDO, and MCI as more proximal antecedents, and the endorsement of discrimination of immigrants in the socioeconomic domain by Russian majority group members as the outcome. Data were collected among 576 participants from 33 regions in Russia, using online social media. MCI predicted endorsement of discrimination of immigrants by Russian majority group members better than did RWA and SDO. SDO predicted only economic aspects of the endorsement of discrimination. The results are discussed within the Russian context, with its ethnically diverse composition of the population and high migration rates.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".