The Evolving Nature of Generalized Prejudice Toward Marginalized Groups in the United States 2004–2020
Bibliographic record
Abstract
Prejudices intercorrelate positively and can be modeled as a generalized prejudice (GP) factor that is considered robust and central to postulating that some people are relatively more prejudiced than others (i.e., prejudice is not purely contextual). Although past research documents changes in specific prejudices over time, the field tacitly assumes GP stability/robustness, an untested notion. Using nationally representative American National Election Survey 2004–2020 data ( N = 21,998) assessing attitudes toward Black people, illegal immigrants, gay people, and feminists, we discovered that prejudices have become increasingly correlated over time. Initially invariant, from 2012 onward GP became variant and required correlated residuals between prejudices (outside of GP). GP vastly increased its association with political conservatism (≈.41 in 2004–2008, ≈.70 by 2016–2020) but less so with age, sex, and education. Indeed, best fit in 2020 involved a “GP 2.0” factor indicated by specific prejudices and conservatism. Implications regarding the nature of prejudice are discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".