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Record W4405837937 · doi:10.1371/journal.pone.0315748

Understanding the pathways to text generation: A longitudinal study on executive functions, oral language, and transcription skills from kindergarten to first grade

2024· article· en· W4405837937 on OpenAlexaff
Juan E. Jiménez, Jennifer Baladé, Eduardo García, Becky Xi Chen

Bibliographic record

VenuePLoS ONE · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicWriting and Handwriting Education
Canadian institutionsUniversity of Toronto
FundersAgencia Estatal de Investigación
KeywordsStructural equation modelingCompetence (human resources)Transcription (linguistics)OrthographyPsychologyNarrativeExecutive functionsLongitudinal studyDevelopmental psychologyCognitionCognitive psychologyLinguisticsMathematicsSocial psychologyStatisticsNeuroscience

Abstract

fetched live from OpenAlex

This longitudinal study explored the contribution of transcription skills, oral language abilities, and executive functions in kindergarten to written production in grade 1 among Spanish-speaking children (N = 191) through structural equation modeling (SEM). Three dimentions of written production were assessed, including productivity, quality, and syntactic complexity. Accordingly, three SEM models were tested to explore these relationships, and the estimated models for each endogenous variable demonstrated good fit. The results indicate that transcription skills and executive functions were key predictors of productivity, while both transcription and narrative oral competence contributed to writing quality. Syntactic complexity, on the other hand, was primarily influenced by narrative oral competence and executive functions. The results are interpreted within the framework of the not-so-simple view of writing model, particularly considering the characteristics of a shallow orthography. Limitations and educational implications are also discussed.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.235
GPT teacher head0.331
Teacher spread0.096 · 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 designQualitative
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

Citations2
Published2024
Admission routes1
Has abstractyes

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