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Record W4380872404 · doi:10.1038/s41598-023-35346-7

Adolescent leisure reading and its longitudinal association with prosocial behavior and social adjustment

2023· article· en· W4380872404 on OpenAlexaff
Jan Lenhart, Tobias Richter, Markus Appel, Raymond A. Mar

Bibliographic record

VenueScientific Reports · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsYork University
Fundersnot available
KeywordsProsocial behaviorReading (process)Association (psychology)PsychologyLongitudinal studyDevelopmental psychologyStructural equation modelingSocial cognitionSocial engagementSocial psychologyCognitionMedicineComputer scienceSociology

Abstract

fetched live from OpenAlex

Reading is a popular leisure activity for children, teenagers, and adults. Several theories agree that reading might improve social cognition, but the empirical evidence remains tentative, with research on adolescents especially lacking. We employed a very large, and nationally representative, longitudinal dataset from the National Educational Panel Study (NEPS) in Germany to examine this hypothesis. Specifically, we tested whether reading prospectively predicted future self-reported prosocial behavior and social adjustment in adolescents, controlling for a number of covariates. Two-way cross-lagged panel analyses probed the longitudinal relationship between leisure reading and these social outcomes from Grade 6 to Grade 9. In addition, we examined the effect of cumulative reading experience across Grades 5-8 on future social outcomes, using structural equation modeling. We also explored the unique contributions of cumulative reading experience in different literary genres (classic literature, popular literature, nonfiction, comic books). Cumulative reading in general did not predict future prosocial behavior and social adjustment. However, cumulative reading of modern classic literature was positively associated with later prosocial behavior and social adjustment. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 08 November 2021. The protocol, as accepted by the journal, can be found at: https://doi.org/10.17605/OSF.IO/KSWY7 .

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
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 head0.029
GPT teacher head0.311
Teacher spread0.282 · 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.

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

Citations20
Published2023
Admission routes1
Has abstractyes

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