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Record W4392881708 · doi:10.1177/13684302241233505

Perceptions of women and men in mixed-race heterosexual relationships

2024· article· en· W4392881708 on OpenAlexafffund
Maria Iankilevitch, Alison L. Chasteen

Bibliographic record

VenueGroup Processes & Intergroup Relations · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of TorontoUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologySocial psychologyRace (biology)Mixed racePerceptionDevelopmental psychologyRomanceGender studies

Abstract

fetched live from OpenAlex

Although the number of mixed-race couples is increasing in North America, these couples continue to experience stigma and discrimination, which can have deleterious effects on individuals in these relationships. In three samples, we examined perceivers' first impressions of targets in mixed-race couples when viewed with their romantic partner versus alone, including their warmth and competence (Sample 1a), global morality (Sample 1b), and specific stereotypic behaviors including likelihood to betray, conform, and be prejudiced (Sample 1c). Partner effects occurred for specific stereotypes relevant for intergroup behaviors such that individuals in mixed-race couples were rated as more likely to betray close others and to be less conforming and less prejudiced than individuals in same-race couples when viewed with their partners. These results suggest that specific stereotypes relevant for intergroup relations are affected by the race of targets' romantic partners and lay the foundation for understanding the unique challenges faced by members of mixed-race couples.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.024
GPT teacher head0.320
Teacher spread0.296 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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