Does Reducing Implicit Prejudice Increase Out-Group Identification? The Downstream Consequences of Evaluative Training on Associations Between the Self and Racial Categories
Bibliographic record
Abstract
The present experiments were designed to investigate whether an intervention that targeted racial attitudes influenced not only prejudice but also self–Black associations. Because past research has demonstrated that people strive to build connections with favorable social categories, we predicted that positive evaluative training would increase identification with Blacks. Results from three studies provide evidence that practice in associating positive concepts with Blacks reduced implicit prejudice which in turn increased implicit self–Black associations. Notably, prejudice, in this case, had an intervening variable effect. Study 3 also investigated the impact of an alternative intervention that directly targeted self-associations rather than racial attitudes. Unlike evaluative training, associating the self with Blacks directly reduced both implicit prejudice and increased self–Black associations. These findings extend theorizing on the causal relationship between prejudice and out-group identification and provide important process information on how particular interventions reduce intergroup biases.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".