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Record W4313417417 · doi:10.1177/0044118x221140515

Sexual Minority Status and Academic Achievement During the Transition to Adolescence Among Youth With Childhood Conduct Problems

2022· article· en· W4313417417 on OpenAlexafffund
Alexa Martin‐Storey, Gabrielle Garon‐Carrier, Michèle Déry, Caroline E. Temcheff

Bibliographic record

VenueYouth & Society · 2022
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsMcGill UniversityUniversité de Sherbrooke
FundersInstitute of Human Development, Child and Youth HealthSocial Sciences and Humanities Research Council of Canada
KeywordsAcademic achievementPsychologyDevelopmental psychologySexual identityStandardized testLanguage artsSexual minoritySexual orientationSocial psychologyMathematics educationHuman sexualitySociologyGender studies

Abstract

fetched live from OpenAlex

Youth with conduct problems have poorer academic outcomes than their typically developing peers. The objective of the current study was to examine how sexual minority status was associated with trajectories of teacher-rated mathematics and language arts (i.e., reading and writing) achievement in seven consecutive years across the transition to adolescence among youth with childhood histories of conduct problems ( N = 383). Sexual minority status (as assessed via indicators of identity, attraction, or behavior during adolescence in the eighth year of the study) was not associated with initial mathematics or language arts performance at time 1, but was associated with declining mathematics achievement during the transition to adolescence. These findings suggest that sexual minority status is linked to change in some aspects academic achievement among youth already at risk for poorer academic achievement (i.e., youth with conduct problems).

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)0.000
metaresearch head score (Gemma)0.002
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 score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.038
GPT teacher head0.300
Teacher spread0.262 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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