Implementation Intentions Reduce Implicit Stereotype Activation and Application
Bibliographic record
Abstract
Research has found that implementation intentions, if-then action plans (e.g., “if I see a Black face, I will think safe”; Stewart & Payne, 2008), reduce stereotyping on implicit measures. However, it is unknown by what process(es) implementation intentions reduce implicit stereotyping. The present research examines the effects of implementation intentions on stereotype activation (e.g., extent to which stereotypic information is accessible) and stereotype application (e.g., extent to which accessible stereotypes are applied in judgment). Additionally, we assessed the efficiency of implementation intentions by manipulating cognitive resources (e.g., digit-span, restricted response-window) while participants made judgments on an implicit stereotyping measure. Across four studies, implementation intentions reduced implicit stereotyping. This decrease in stereotyping was associated with reductions in both stereotype activation and application. Additionally, these effects of implementation intentions were highly efficient and associated with reduced stereotyping even for groups for which people may have little practice inhibiting stereotypes (e.g., gender).
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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 | 0.002 | 0.011 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".