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Record W2978191518 · doi:10.1037/emo0000483

Timing of adolescent emotional disclosures: The role of maternal emotions and adolescent age.

2018· article· en· W2978191518 on OpenAlexfundno aff
Alexandra Main, Jessica P. Lougheed, Janice Disla, Smitha Kashi

Bibliographic record

VenueEmotion · 2018
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of California BerkeleyPsi Chi
KeywordsPsycINFOPsychologyAffect (linguistics)Developmental psychologySelf-disclosureClinical psychologyObservational studySocial psychologyMEDLINEMedicine

Abstract

fetched live from OpenAlex

= 14.84 years) and their mothers participated in a 10-min conflict discussion. Adolescent emotional disclosures and maternal emotions were coded moment-to-moment. Results from survival analysis demonstrated that older adolescents whose mothers expressed high levels of negative affect or high levels of validation were more likely to make emotional disclosures earlier in the discussion than were older adolescents whose mothers expressed low negative affect or low validation. There were no differences in associations between maternal emotions and the timing of emotional disclosures for younger adolescents. Findings suggest that a range of maternal emotions (validation and negative affect) might be features of high-quality mother-adolescent relationships in older adolescence, when parent-adolescent relationships are more egalitarian and negative emotions may be more readily expressed. Implications for applying observational methodologies and dynamic statistical techniques to the adolescent disclosure literature are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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)0.002
metaresearch head score (Gemma)0.011
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 score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.022
GPT teacher head0.281
Teacher spread0.258 · 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

Citations22
Published2018
Admission routes1
Has abstractyes

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