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Record W2910066937 · doi:10.1177/2156869318820092

Revisiting the Cost of Skin Color: Discrimination, Mastery, and Mental Health among Black Adolescents

2019· article· en· W2910066937 on OpenAlexaff
Patricia Louie

Bibliographic record

VenueSociety and Mental Health · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTone (literature)Association (psychology)PsychologyMental healthDepression (economics)Set (abstract data type)Clinical psychologyDevelopmental psychologyAudiologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

This article investigates the association between skin tone and mental health in a nationally representative sample of black adolescents. The mediating influences of discrimination and mastery in the skin tone–mental health relationship also are considered. Findings indicate that black adolescents with the darkest skin tone have higher levels of depressive symptoms than their lighter skin tone peers. This is not the case for mental disorder. For disorder, a skin tone difference appeared only between black adolescents with very dark skin tone and black adolescents with medium brown skin tone. Discrimination partially mediates the association between skin tone and depression, while mastery fully mediates this association, indicating that the impact of skin tone on depression operates primarily through lower mastery. Similar patterns were observed for disorder. By extending the discussion of skin tone and health to black adolescents and treating skin tone as a set of categories rather than a linear gradient, I provide new insights into the patterning of skin tone and depression/disorder.

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 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 score0.112
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.029
GPT teacher head0.372
Teacher spread0.343 · 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 teacher head, 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

Citations37
Published2019
Admission routes1
Has abstractyes

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