A two-dimensional model that employs explicit and implicit attitudes to characterize prejudice.
Bibliographic record
Abstract
In the authors' 2-dimensional model of prejudice, explicit and implicit attitudes are used to create 4 profiles: truly low prejudiced (TLP: double lows), aversive racists (AR: low explicit modern racism/high implicit prejudice), principled conservatives (PC: high explicit modern racism/low implicit prejudice), and modern racists (MR: double highs). Students completed an Asian Modern Racism Scale and an Asian/White Implicit Association Test. The authors compared the 4 groups' prejudice-related ideologies (i.e., egalitarianism/humanism and social conservatism) and economic/political conservatism (Study 1, N=132). The authors also tested whether MR but not PC (Study 2, N=65) and AR but not TLP (Study 3, N=143) are more likely to negatively evaluate an Asian target when attributional ambiguity is high (vs. low). In support of the model, TLP did not hold prejudice-related ideologies and did not discriminate; AR were low in conservatism and demonstrated the attributional-ambiguity effect; PC did not strongly endorse prejudice-related ideologies and did not discriminate; MR strongly endorsed prejudice-related ideologies, were conservative, and demonstrated the attributional-ambiguity effect. The authors discuss implications for operationalizing and understanding the nature of prejudice.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".