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Record W2913845982 · doi:10.3138/jcfs.49.4.461

Daily Time-Use Patterns, Psychological Well-Being, and Family Socioeconomic Status of South Korean Adolescents: A Mixture Modeling

2018· article· en· W2913845982 on OpenAlexvenueno aff
Jiyeon Lee, Kyungsun Yang, Yuen Mi Cheon

Bibliographic record

VenueJournal of Comparative Family Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsSocioeconomic statusDemographyPsychologyAssociation (psychology)Developmental psychologyPopulationSociology

Abstract

fetched live from OpenAlex

The current study sought to identify different types of South Korean adolescents’ time-use patterns across various daily activities, compare the levels of psychological well-being among the identified groups, and examine the association between daily time-use patterns and family socioeconomic status. By using the fifth wave of Korean Children and Youth Panel Survey, the data of 1,764 eighth grade students currently living in two-parent families were analyzed using a mixture modeling. The best fitting model revealed four types of time-use patterns: (1) Study Hard (2) No Hagwon, but Independent Studies (3) Only Hagwon, and (4) No study, Skewed toward Entertainment. Differences in the level of psychological well-being were found for different time-use types. Moreover, the probability of being included in each type varied according to family socioeconomic status. The results demonstrated the role of family socioeconomic status in adolescents’ time-use patterns and verified the differences in the level of psychological well-being according to how adolescents used their daily time.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.066
GPT teacher head0.376
Teacher spread0.310 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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