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Record W4412370141 · doi:10.15628/holos.2025.18418

DESENVOLVIMENTO DO GOSTO PELA POESIA E PELA MATEMÁTICA EM CRIANÇAS DE 5 ANOS

2025· article· pt· W4412370141 on OpenAlexfundno aff
Raquel Pereira, Pedro Palhares, Fernando Azevedo

Bibliographic record

VenueHolos · 2025
Typearticle
Languagept
FieldSocial Sciences
TopicEducation Pedagogy and Practices
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaInternational Council for Canadian Studies
KeywordsHumanitiesPhysicsPhilosophy

Abstract

fetched live from OpenAlex

Desenvolveu-se um estudo qualitativo que se centra na articulação entre educação literária, linguística e matemática através da poesia e que tem como um dos seus objetivos promover o desenvolvimento do gosto pela matemática e pela poesia. Existe uma estreita relação entre a motivação e a aprendizagem, pelo que é relevante o investimento no desenvolvimento do gosto pela matemática e pela poesia, desde a infância. Apresenta-se um recorte do estudo, focado no desenvolvimento do mesmo com crianças de 5 anos em contexto de educação pré-escolar, ao longo de seis meses. A partir da mobilização de poemas de qualidade literária e com potencialidade para promover a articulação das áreas referidas, foram construídos e desenvolvidos em contexto percursos de aprendizagem. A análise dos dados resultantes da investigação aponta para que as estratégias de articulação implementadas tenham, de facto, contribuído para fomentar o gosto das crianças pela poesia e pela matemática.

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)0.004
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.054
GPT teacher head0.431
Teacher spread0.378 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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