When trying to understand detracts from trying to behave: Effects of perspective taking in intergroup interaction.
Bibliographic record
Abstract
Four studies demonstrate that perspective taking can backfire in intergroup interaction, leading lower prejudice individuals to treat an outgroup member less positively than they do when they adopt alternative mind-sets; for higher prejudice individuals, perspective taking instead had a positive, albeit less consistent, effect on behavior. The net result was behavior disruption, whereby individuals' treatment of an outgroup member became incongruent with their inner attitudes. This disruption effect was evident for cognitive and affective forms of perspective taking, in ostensible and real face-to-face intergroup interactions, and for feelings of happiness experienced by individuals' interaction partner as well as outside observers' behavior assessments. Results further suggested that self-regulatory effort mediated the effect of perspective taking on intergroup interaction behavior, with the negative consequences of perspective taking for lower prejudice individuals' behavior appearing to stem from complacency rather than trying too hard. Overall, the findings reveal that perspective taking rather than self-focus accounts for the cognitive resource depletion and behavior disruption effects previously demonstrated to stem from evaluative concern in intergroup interaction and indicate that perspective taking may be more reliably helpful outside of intergroup interaction situations than within them.
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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 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".