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Record W2973044864 · doi:10.1080/1350293x.2019.1678924

Impact of interactive reading intervention on narratives skills on children with low socio-economic background

2019· article· en· W2973044864 on OpenAlexaboutno aff
Nathalie Thomas, Cécile Colin, Jacqueline Leybaert

Bibliographic record

VenueEuropean Early Childhood Education Research Journal · 2019
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativePsychologyReading (process)Intervention (counseling)Developmental psychologySentenceLinguistics

Abstract

fetched live from OpenAlex

Narrative skills are highly predictive of linguistic development as well as future school performances. Yet, children with low socio-economic status (SES) background present specific difficulties for these skills. Interactive reading sessions could have beneficial effects on narrative capacities. We analyze the effects of an IR intervention program on the narratives of children from a low SES, on macrostructural and microstructural parameters. Thirty IR sessions were proposed to 172 children. A control group (N = 87) benefited of the usual activities of class. Narrative skills were measured with the Edmonton Narrative Norms Instrument (ENNI) and productions were transcribed via CHILDES. The results did not highlight a significant difference between groups concerning the macrostructural parameters. However microstructural parameters improved significantly in the experimental group as regards lexical, discursive and sentence components.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.018
GPT teacher head0.380
Teacher spread0.362 · 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 designNon-randomized trial
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

Citations11
Published2019
Admission routes1
Has abstractyes

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