Examining prejudice‐reduction theories in anti‐racism initiatives
Bibliographic record
Abstract
Abstract We examined the role of prejudice‐reduction theories in anti‐racism initiatives, by identifying, gathering, and analyzing anti‐racism campaigns from a variety of English‐language websites. Our review revealed many anti‐racism initiatives running in the United Kingdom and a smaller number of initiatives running in Canada, the United States, Australia, and other countries. We provide a description of the key themes and messages being used in anti‐racism initiatives, including a variety of components such as TV and radio public service announcements, print materials, social events, competitions, awards, and help‐services. We also discuss how the components of the initiatives correspond with Duckitt's ( 2001 ) multi‐level framework for prejudice reduction, operating on four causal levels: (1) perceptual‐cognitive, (2) individual, (3) interpersonal, and (4) societal‐intergroup. Recommendations for enhancing future anti‐racism initiatives are provided. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".