On the Self-Regulation of Implicit and Explicit Prejudice
Bibliographic record
Abstract
The present study identifies a broad taxonomy of motives underlying the desire to regulate prejudice and assess the impact of motivation to regulate prejudice on levels of explicit and implicit prejudice. Using self-determination theory as the foundation, six forms of motivation to regulate prejudice are proposed. In Study 1 (N = 257), an exploratory factor analysis reveals evidence for the six proposed dimensions. In Study 2 (N = 198), the six-factor taxonomy of motivation to regulate prejudice is further validated using a confirmatory factor analysis, and construct validity is obtained. In Study 3 (N = 62), motivation to regulate prejudice is manipulated before participants complete the Implicit Association Test (IAT) and explicit measures of prejudice. Results reveal that those with highly self-determined regulation of prejudice demonstrate lower implicit and explicit prejudice than their less self-determined counterparts. Results are discussed in terms of an increased understanding of the motivation to control 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 | 0.002 | 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) | 0.001 | 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 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".