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Record W1644965522 · doi:10.1111/josi.12059

Stress and Coping in Interracial Contexts: The Influence of Race‐Based Rejection Sensitivity and Cross‐Group Friendship in Daily Experiences of Health

2014· article· en· W1644965522 on OpenAlexafffund
Elizabeth Page‐Gould, Rodolfo Mendoza‐Denton, Wendy Berry Mendes

Bibliographic record

VenueJournal of Social Issues · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsUniversity of Toronto
FundersNational Heart, Lung, and Blood InstituteSocial Sciences and Humanities Research Council of Canada
KeywordsFriendshipCoping (psychology)Race (biology)PsychologySocial psychologyClinical psychologyGerontologyDevelopmental psychologyMedicineSociologyGender studies

Abstract

fetched live from OpenAlex

We examined the interplay of psychosocial risk and protective factors in daily experiences of health. In Study 1, the tendency to anxiously expect rejection from racial outgroup members, termed race-based rejection sensitivity (RS-race), was cross-sectionally related to greater stress-symptoms among Black adults who reported fewer cross-race friends but not among participants who had more cross-race friends. In Study 2, we experimentally manipulated the development of a same- versus cross-race friendship among Latino/a-White dyads prior to collecting daily experiences of stress-symptoms using a diary methodology. While RS-race predicted more psychosomatic symptoms in the same-race friendship condition, RS-race was unrelated to symptomatology among participants who made a cross-race friend. These findings suggest that experiences of intergroup stress can spill over into everyday life in the absence of positive contact, but cross-race friendships may be a resource that mitigates the expression of interracial stress.

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)0.002
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.036
GPT teacher head0.416
Teacher spread0.380 · 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

Citations54
Published2014
Admission routes2
Has abstractyes

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