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Record W2791480764 · doi:10.4322/gepem.2017.035

O conceito de divisão no modo de organização do ensino proposto por davýdov e colaboradores

2017· article· pt· W2791480764 on OpenAlexaff
Josélia Euzébio da Rosa, Sandra Crestani

Bibliographic record

VenueBoletim GEPEM · 2017
Typearticle
Languagept
FieldSocial Sciences
TopicEducation Pedagogy and Practices
Canadian institutionsGeomechanica (Canada)
Fundersnot available
KeywordsPhilosophyPolitical sciencePsychology

Abstract

fetched live from OpenAlex

Investigamos o modo de organização de ensino proposto por Davýdov com a finalidade de promover o desenvolvimento do pensamento teórico dos estudantes por meio da apropriação dos conceitos científicos. Davýdov propõe que o ensino seja organizado por meio de tarefas de estudos, constituídas de seis ações, cujo desenvolvimento ocorre durante a resolução de um sistema de tarefas particulares. Em sua concepção, todos os conceitos são constituídos por uma relação universal. Nessa proposição, em uma pesquisa de caráter bibliográfico, analisamos as manifestações da relação universal do conceito de divisão. Constatamos que tal relação é revelada no movimento de modelação que segue do plano objetal ao gráfico e literal por meio da unidade básica, intermediária e o total de ambas. A gênese do conceito, na interconexão desses elementos, desencadeia um movimento conceitual orientado do geral para o particular e singular por meio da inter-relação das significações algébricas, geométricas e aritméticas.

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)0.007
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0070.017
Scholarly communication0.0130.015
Open science0.0020.011
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0060.002

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 head0.060
GPT teacher head0.410
Teacher spread0.350 · 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 designTheoretical or conceptual
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
Published2017
Admission routes1
Has abstractyes

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