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Record W6887708860 · doi:10.17605/osf.io/2fhm4

Internalized Racism, Family Social Support, Ethnic-Racial Centrality, and Self-Esteem Among BIPOC Canadian Young Adults

2024· other· en· W6887708860 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsStressorRacismMental healthSocial supportYoung adultLongitudinal studyRacial differencesProtective factorQuality of Life Research

Abstract

fetched live from OpenAlex

Black and Latinx individuals frequently experience ethnic-racial discrimination, a harmful stressor that leads to increased depressive symptoms and lower psychological well-being. Recently, scholars have argued that internalized racism, or negative beliefs about one’s racial group, may be the most harmful form of racism (Seaton, 2020). The adverse negative mental health effects of internalized racism have been extensively documented, however, internalized racism’s impact on aspects of psychological well-being (e.g., self-esteem) and development of a strong ethnic-racial identity, a developmental milestone among people of colour (Umaña-Taylor et al., 2014), have not been as well-documented – particularly in a longitudinal fashion. Further, familial social support is a known protective factor against the adverse effects of in-person racism, however, less research has documented its ability to protect against the stress of internalized racism. The present pre-registered study addresses these gaps by examining the short-term longitudinal (6-month) impacts of internalized racism on self-esteem and ethnic-racial centrality. Additionally, we will examine the main effect and the moderating influence of familial social support to determine whether it can mitigate the hypothesized adverse effects of internalized racism on our 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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.339
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0010.004
Scholarly communication0.0050.001
Open science0.0070.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.011

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 head0.027
GPT teacher head0.347
Teacher spread0.320 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueOpen Science FrameworkFrench-language works237,207