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Record W2022149058 · doi:10.1111/ncmr.12005

When White Feels Right: The Effects of In‐Group Affect and Race of Partner on Negotiation Performance

2013· article· en· W2022149058 on OpenAlex
Debra Gilin Oore, Annette Gagnon, David Bourgeois

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNegotiation and Conflict Management Research · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicConflict Management and Negotiation
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsNegotiationAffect (linguistics)DyadSocial psychologyWhite (mutation)PsychologyMeaning (existential)Race (biology)Political scienceSociologyGender studiesCommunicationLaw

Abstract

fetched live from OpenAlex

Abstract This research investigated the unique role of racial in‐group affect, or liking one's racial group, to foster or inhibit integration in negotiations with different race partners. We hypothesized that when the racial backgrounds of the negotiators are salient, threat inherent in negotiations activates in‐group affect for some White negotiators (those more “glad to be White”), triggering divergent negotiation approaches with White versus Black counterparts. In support of our hypotheses, we found that when negotiating with a Black confederate, stronger in‐group affect of White participants was a liability, relating to poorer joint outcomes and a “chilling and competing” negotiation approach. When negotiating with a White confederate, stronger in‐group affect of White participants instead boosted the dyad's joint outcomes by fostering greater trust. The meaning and practical implications of strong in‐group affect in negotiations with diverse counterparts are discussed.

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.

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)0.002
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 score0.631
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.031
GPT teacher head0.336
Teacher spread0.304 · 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