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Record W2037169741 · doi:10.1177/0272431613507498

Developmental Change and Time-Specific Variation in Global and Specific Aspects of Self-Concept in Adolescence and Association With Depressive Symptoms

2013· article· en· W2037169741 on OpenAlexaff
Yaşar Kuzucu, Daniel E. Bontempo, Scott M. Hofer, Michael C. Stallings, Andrea M. Piccinin

Bibliographic record

VenueThe Journal of Early Adolescence · 2013
Typearticle
Languageen
FieldPsychology
TopicEducation, Achievement, and Giftedness
Canadian institutionsUniversity of Victoria
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute on Deafness and Other Communication Disorders
KeywordsPsychologyVariation (astronomy)Depressive symptomsDevelopmental psychologyAssociation (psychology)Competence (human resources)PerceptionLatent growth modelingClinical psychologySocial psychologyCognitionPsychotherapist

Abstract

fetched live from OpenAlex

Previous research has demonstrated that adolescents make differential self-evaluations in multiple domains that include physical appearance, academic competence, and peer acceptance. We report growth curve analyses over a seven year period from age 9 to age 16 on the six domains of the Harter Self-Perception Profile for Children. In general, we find little change in self-concept, on average, but do find substantial individual differences in level, rate of change, and time-specific variation in these self- evaluations. The results suggest that sex differences and adoptive status were related to only certain aspects of the participants' self-concept. Depressive symptoms were found to have significant effects on individual differences in rate of change and on time-specific variation in general self-concept, as well as on some of the specific domains of self-concept.

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)0.006
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.249
Teacher spread0.233 · 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

Citations48
Published2013
Admission routes1
Has abstractyes

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