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Record W2916165066 · doi:10.5334/pb.467

Daily Affect and Self-Esteem in Early Adolescence: Correlates of Mean Levels and Within-Person Variability

2019· article· en· W2916165066 on OpenAlexafffund
Sabine Nelis, William M. Bukowski

Bibliographic record

VenuePsychologica Belgica · 2019
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of CanadaKU Leuven
KeywordsPsychologyAffect (linguistics)AggressionSelf-esteemDevelopmental psychologyAssociation (psychology)Clinical psychology

Abstract

fetched live from OpenAlex

Emotions and self-esteem are critical components of well-being and adaptation during adolescence. People differ in their average levels of affect and self-esteem, as well as in how much their affect and self-esteem fluctuate from moment to moment. Fluctuations in affect in particular have not been extensively examined in relation to adolescent-relevant variables. The present study investigates internalizing symptoms, social functioning, and overt and relational aggression as correlates of average levels and within-person variability in daily positive and negative affect (PA and NA) and self-esteem. Crucially, unique association were examined controlling for the other variables. Early adolescents (mean age 10.8 years, N = 94) completed daily diaries across four days on PA, NA, and self-esteem. They also completed general questionnaires, as did peers. Some key findings were that more internalizing symptoms were significantly associated with more variability in NA. The importance of peer relationships for adolescents’ daily mean levels of PA and NA were shown. Peer-perceived social functioning was associated with less fluctuations in self-esteem. Some unexpected, non-significant, findings for aggression appeared. Finally, higher mean NA were associated with more NA fluctuations, whereas higher mean PA and self-esteem were associated with less fluctuations.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.019
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

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

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 head0.023
GPT teacher head0.275
Teacher spread0.252 · 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 teacher head, 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

Citations24
Published2019
Admission routes2
Has abstractyes

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