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Record W3199194511 · doi:10.1177/13684302211040864

Race, social pain minimization, and mental health

2021· article· en· W3199194511 on OpenAlex

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

VenueGroup Processes & Intergroup Relations · 2021
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPsychologyMental healthRace (biology)DistressSocial supportClinical psychologyWhite (mutation)Social environmentPsychological painPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

People often believe Black individuals experience less social pain and require less social support to cope with distress than White individuals (e.g., Deska, Kunstman, Lloyd, et al., 2020). However, researchers have not tested whether biases in third-person pain judgments translate to first-person experiences with social pain minimization. For example, do Black individuals feel their social pain is underrecognized to a greater extent than White individuals? The current work tested whether Black individuals felt their social pain was minimized more than White individuals and if the experience of social pain minimization was related to worse mental health and greater life stress. Data from two cross-sectional, correlational studies provide initial support for these predictions ( N total = 1,501). Black participants felt their social pain was minimized more than White participants and this race difference in social pain minimization was associated with worse mental health and greater life stress. These results suggest that Black individuals feel their pain is underrecognized and this experience of social pain minimization is related to worse mental health outcomes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.737
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.298
Teacher spread0.285 · 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