MétaCan
Menu
Back to cohort

A CONTRIBUIÇÃO DOS CONTOS DE FADAS NO PROCESSO DE ENSINO EAPRENDIZAGEM DAS CRIANÇAS

2018· article· en· W2922171559 on OpenAlexaff
Ademir Henrique Manfré, Adriana Prado, Fernanda Simões Machado

Bibliographic record

VenueColloquium humanarum · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicScience and Education Research
Canadian institutionsBibliographical Society of Canada
Fundersnot available
KeywordsTheme (computing)StorytellingElaborationPsychologyValue (mathematics)Dimension (graph theory)Process (computing)Reflection (computer programming)EpistemologySociologyPedagogyHumanitiesPhilosophyComputer scienceLinguisticsNarrative

Abstract

fetched live from OpenAlex

This work has as its theme the contribution of fairy tales in the process of learning of children, addressing in a significant way the importance that the fairy tales exert in the pedagogical dimension and in what aspects can favor the development of the child. It presents a qualitative approach that favors reflection, analysis and interaction about the theories and hypotheses raised.The question that motivated the choice of this theme was: How fairy tales can contribute to the development of the child. The bibliographical research was the one that supported the whole elaboration of this work, in which were used conceptions of important authors of children's literature. From the literary review, it was possible to perceive that, although storytelling presents itself as a rich medium for the development of children's abilities, teachers are generally unaware of their value as a support in the teaching-learning process.

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)0.014
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.054
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0060.008
Scholarly communication0.0090.005
Open science0.0020.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.044
GPT teacher head0.452
Teacher spread0.409 · 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 designNot applicable
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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueColloquium humanarumSame topicScience and Education ResearchFrench-language works237,207