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Record W2117281209 · doi:10.1037/a0013411

When trying to understand detracts from trying to behave: Effects of perspective taking in intergroup interaction.

2009· article· en· W2117281209 on OpenAlexafffund
Jacquie D. Vorauer, Verena Martens, Stacey J. Sasaki

Bibliographic record

VenueJournal of Personality and Social Psychology · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyOutgroupPerspective (graphical)Social psychologyPrejudice (legal term)Perspective-takingFeelingCognitionHappinessEmpathy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.641

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.071
GPT teacher head0.436
Teacher spread0.365 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations128
Published2009
Admission routes2
Has abstractyes

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