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Record W2767714397 · doi:10.1352/1944-7558-122.6.539

Self-Esteem Trajectories and Their Social Determinants in Adolescents With Different Levels of Cognitive Ability

2017· article· en· W2767714397 on OpenAlexaff
Alexandre J. S. Morin, A. Katrin Arens, Danielle Tracey, Philip D. Parker, Joseph Ciarrochi, Rhonda Craven, Christophe Maïano

Bibliographic record

VenueAmerican Journal on Intellectual and Developmental Disabilities · 2017
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversité du Québec en OutaouaisConcordia University
Fundersnot available
KeywordsSelf-esteemCognitionPsychologyDevelopmental psychologyAdolescent developmentDemographyPeer groupPsychiatry

Abstract

fetched live from OpenAlex

This study examines the development of self-esteem in a sample of 138 Australian adolescents (90 males; 48 females) with cognitive abilities in the lowest 15% (L-CA) and a matched sample of 556 Australian adolescents (312 males; 244 females) with average to high levels of cognitive abilities (A/H-CA). These participants were measured annually (Grade 7 to 12). The findings showed that adolescents with L-CA and A/H-CA experience similar high and stable self-esteem trajectories that present similar relations with key predictors (sex, school usefulness and dislike, parenting, and peer integration). Both groups revealed substantial gender differences showing higher levels of self-esteem for adolescent males remaining relatively stable over time, compared to lower levels among adolescent females which decreased until midadolescence before increasing back.

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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.292
Teacher spread0.261 · 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

Citations11
Published2017
Admission routes1
Has abstractyes

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