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Record W4413524139 · doi:10.21814/childstudies.6347

The use of short stories to improve literacy in a bilingual context

2025· article· pt· W4413524139 on OpenAlexfundno aff
Aldora Astreia Cadete

Bibliographic record

VenueChild Studies · 2025
Typearticle
Languagept
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaUniversidade do MinhoInternational Council for Canadian Studies
KeywordsContext (archaeology)LiteracyPsychologyLinguisticsComputer scienceMathematics educationSociologyPedagogyHistoryPhilosophyArchaeology

Abstract

fetched live from OpenAlex

Este estudo decorre de um projeto de investigação de doutoramento em curso e apresenta resultados preliminares sobre o papel do conto na melhoria da literacia de alunos do 6.º ano em Angola, especialmente numa zona rural caracterizada pelo bilinguismo. O trabalho envolveu 39 alunos, sete pais e dois professores. O tema central foi a leitura de contos. Adotámos um paradigma de investigação qualitativa complementado por uma análise quantitativa limitada. A análise de conteúdo e a estatística descritiva foram utilizadas como métodos de estudo. O estudo constatou um baixo nível de literacia entre os alunos, a falta de livros didáticos sobre textos literários, o facto de a língua materna dos alunos não ser a mesma que a língua de ensino e a falta de especialização dos professores no ensino do português. Uma conclusão preliminar é a falta de utilização do conto como estratégia de ensino, o que tem dificultado o processo de ensino-aprendizagem.

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)0.014
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.123
GPT teacher head0.455
Teacher spread0.332 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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