Two Forms of Racism and Their Related Outcomes: The Bad and the Ugly.
Bibliographic record
Abstract
Two experiments investigated the related outcomes of two forms of racism among college students (413 in the first and 374 in the second experiment) enrolled in a program leading to careers in law enforcement such as police officers. The two forms of racism were the overt, traditional type whereby visible minorities are denigrated on the basis of innate characteristics, and the subtle type called neoracism, which incorporates egalitarian values and negative beliefs in the blame of visible minorities for undeserved gains and overall social problems. The design of the experiments allowed for a reality check in that they pertained to racial issues relevant to training and work in law enforcement. Experiment 1 showed, as hypothesized, that although both forms of racism are linked, only neoracism is associated with covert attitudes (i.e., unfavourable reactions to employment equity), and traditional racism is related to overt discriminatory behavioural intentions. Experiment 2 investigated the impact of priming a negative reaction to a visible minority on the pattern of these links. Under such conditions the observed links strongly suggest that respondents regress to old norms as neoracism is then associated with both covert negative attitudes and overt discriminatory behavioural intentions. The theoretical and practical implications of these findings 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".