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Record W2051075990 · doi:10.1177/1046496405284382

Interpersonal Affect and Peer Rating Bias in Teams

2006· article· en· W2051075990 on OpenAlexaff
Simon Taggar, Travor C. Brown

Bibliographic record

VenueSmall Group Research · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicConflict Management and Negotiation
Canadian institutionsMemorial University of NewfoundlandWilfrid Laurier University
Fundersnot available
KeywordsPsychologyAffect (linguistics)Interpersonal communicationInter-rater reliabilitySocial psychologyArbitrationPeer feedbackInterpersonal relationshipPeer evaluationDevelopmental psychologyRating scaleHigher educationCommunication

Abstract

fetched live from OpenAlex

This study assessed the consequences of performance feedback received from peers on a team member’s subsequent ratings of others, and the mediating influence of interpersonal affect. Undergraduate participants ( N = 142) working in 30 teams during a 7-week period were assigned collective bargaining and arbitration tasks. We found that a team member’s prior positive or negative peer feedback resulted in increased leniency or severity, respectively, and increased restriction in range when these same members subsequently rated fellow team members. Interrater agreement on ratings of peers at Time 3 was higher when raters received similar feedback (i.e., both received positive or negative feedback) from their peers at the Time 1. The mechanism through which feedback at Time 1 influenced rating biases at Time 3 was found to be interpersonal affect (measured at Time 2).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.171
GPT teacher head0.420
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), 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

Citations28
Published2006
Admission routes1
Has abstractyes

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