On the Roles of Stereotype Activation and Application in Diminishing Implicit Bias
Bibliographic record
Abstract
Stereotypes can influence social perception in undesirable ways. However, activated stereotypes are not always applied in judgments. The present research investigated how stereotype activation and application processes impact social judgments as a function of available resources for control over stereotypes. Specifically, we varied the time available to intervene in the stereotyping process, and used multinomial modeling to independently estimate stereotype activation and application. As expected, social judgments were less stereotypic when participants had more time to intervene. In terms of mechanisms, stereotype application, and not stereotype activation, corresponded with reductions in stereotypic biases. With increasing time, stereotype application was reduced, reflecting the fact that controlling application is time-dependent. In contrast, stereotype activation increased with increasing time, apparently due to increased engagement with stereotypic material. Thus, stereotype activation was highest when judgments were least stereotypical. Thus, reduced stereotyping may coincide with increased stereotype activation if stereotype application is simultaneously decreased.
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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 | 0.007 | 0.029 |
| 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 | 0.001 |
| 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".