Perspective-takers behave more stereotypically.
Bibliographic record
Abstract
Nine studies demonstrated that perspective-takers are particularly likely to adopt a target's positive and negative stereotypical traits and behaviors. Perspective-takers rated both positive and negative stereotypic traits of targets as more self-descriptive. As a result, taking the perspective of a professor led to improved performance on an analytic task, whereas taking the perspective of a cheerleader led to decreased performance, in line with the respective stereotypes of professors and cheerleaders. Similarly, perspective-takers of an elderly target competed less compared to perspective-takers of an African American target. Including the stereotype in the self (but not liking of the target) mediated the effects of perspective-taking on behavior, suggesting that cognitive and not affective processes drove the behavioral effects. These effects occurred using a measure and multiple manipulations of perspective-taking, as well as a panoply of stereotypes, establishing the robustness of the link between perspective-taking and stereotypical behavior. The findings support theorizing (A. D. Galinsky, G. Ku, & C. S. Wang, 2005) that perspective-takers utilize information, including stereotypes, to coordinate their behavior with others and provide key theoretical insights into the processes of both perspective-taking and behavioral priming.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".