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The Moderating Role of Ethnic Identity and Social Support on Relations Between Well‐Being and Academic Performance<sup>1</sup>

2007· article· en· W2010623583 on OpenAlexaff
Barbara Cole, Kimberly Matheson, Hymie Anisman

Bibliographic record

VenueJournal of Applied Social Psychology · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsEthnic groupStereotype threatPsychologySocial psychologySocial supportStereotype (UML)AnxietySocial identity theoryIdentity (music)Academic achievementSocial approvalClinical psychologyDevelopmental psychologySocial groupPolitical science

Abstract

fetched live from OpenAlex

The role of social support and ethnic identity in moderating the effects of factors that may emanate from stereotype threat on academic performance was examined. Depressive and anxiety symptoms of ethnic minority (n = 65) and Euro‐Caucasian students (n = 198) were tracked through their first year of university. Although students' symptoms did not differ at the outset of the year, higher symptoms uniquely evident among ethnic minority students at midyear were associated with poorer final grades, and reduced well‐being was sustained at the end of the year. Social support from friends and fewer unsupportive interactions predicted greater success among ethnic minority students. Although both groups benefited from academic support, such support was perceived as less available to minority students.

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)0.004
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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.068
GPT teacher head0.466
Teacher spread0.398 · 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

Citations74
Published2007
Admission routes1
Has abstractyes

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