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Effects of Perspective Taking and Values Consistency in Reducing Implicit Racial Bias

2023· article· en· W4319074228 on OpenAlexaboutno aff
Carmen Lúcia Colomé Beck, Yors García, Robyn Catagnus

Bibliographic record

VenueUniversitas Psychologica · 2023
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMultivariate analysis of varianceRepeated measures designPerspective (graphical)Perspective-takingEmpathyLikert scaleConsistency (knowledge bases)Social psychologyClinical psychologyAnalysis of varianceDevelopmental psychologyStatisticsMathematics

Abstract

fetched live from OpenAlex

The current study explored the effect of perspective taking and values consistency tasks on reducing implicit racial bias. Using a repeated measures design with a control group, 39 participants, 20 female and 19 males aged from 18-54 years, who identified as White were administered the Implicit Relational Assessment Procedure. All participants in the experimental group (n = 19) and control group (n = 20) completed the Toronto Empathy Questionnaire, Modified Modern Racism Scale, Valuing Questionnaire, and a Likert scale. Experimental group participants completed brief values consistency and perspective taking tasks, whereas the control group completed a guided task. A 2 x 4 mixed repeated-measures analysis of variance was conducted to determine if there was interaction effect between group and trial types and a MANOVA to identify differences in the explicit measures between both groups. Results showed that after the values work and perspective taking exercises, participants in the experimental group recorded shorter mean responses for Inconsistent-Black trial blocks versus Consistent-Black trial blocks compared to the control group. Additionally, a statistically significant impact for interaction between condition and trial type was found for the Consistent-White trial type in the experimental group. Recommendations for future research are presented.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.541

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.095
GPT teacher head0.375
Teacher spread0.280 · 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 designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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