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Record W2151237704 · doi:10.1177/0044118x11424915

The Impact of Perceived Discrimination and Social Support on the School Performance of Multiethnic Sexual Minority Youth

2011· article· en· W2151237704 on OpenAlexaff
Shelley L. Craig, Mark Scott Smith

Bibliographic record

VenueYouth & Society · 2011
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of Toronto
FundersCenters for Disease Control and Prevention
KeywordsPath analysis (statistics)PsychologySexual minorityStructural equation modelingMultivariate analysisDevelopmental psychologySocial psychologySexual orientationMedicine

Abstract

fetched live from OpenAlex

Sexual minority youth are known to face increased risk of poor school performance; however, little research has focused on the educational experiences of multiethnic sexual minority youth (MSMY) in particular. Using venue-based sampling approaches, this study surveyed 255 MSMY at 15 urban high schools. The majority of participants identified as female (65%), Latina (58%), and bisexual (41%), with a mean age of 16. The use of structural equation modeling techniques found that 23% of the variance of school performance was explained by the multivariate model. Examination of the model path coefficients revealed that experiences of perceived discrimination had a powerfully negative influence on the school performance of MSMY. Whereas increased family support was associated with better school performance, neither peer nor school support had similar impact. In addition, levels of support did not significantly moderate the effect of perceived discrimination on MSMY.

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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.100
GPT teacher head0.360
Teacher spread0.260 · 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

Citations56
Published2011
Admission routes1
Has abstractyes

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