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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".