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A escrita nos procedimentos de resolução de problemas de adição e subtração: um processo construtivo

2020· article· pt· W3087452110 on OpenAlexaff
Susana Wolman

Bibliographic record

VenueVeras · 2020
Typearticle
Languagept
FieldArts and Humanities
TopicLinguistics and Education Research
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsHumanitiesPhilosophyPhysicsPsychology

Abstract

fetched live from OpenAlex

Este trabalho analisa o papel desempenhado pela escrita nos procedimentos que os alunos empregam para resolver adições e subtrações. Para tal, nos baseamos em uma pesquisa didática, na qual foram estudadas intervenções docentes e sua implicação na aquisição e no progresso de procedimentos numéricos não convencionais em crianças do primeiro ano da escola primária2 . Uma questão essencial, contemplada no delineamento das situações estudadas na pesquisa citada, é a função que a anotação desempenha. No momento da resolução, pede-se aos alunos que registrem como resolveram o problema. O objetivo disso é estimular que as crianças explicitem seus procedimentos por meio da utilização de seus próprios modos de representação gráfica, e possam relembrar o que foi feito no momento da discussão em grupo, posterior ao da resolução.

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)0.017
metaresearch head score (Gemma)0.056
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.056
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.004
Science and technology studies0.0070.012
Scholarly communication0.0160.017
Open science0.0040.009
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0130.003

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 head0.079
GPT teacher head0.311
Teacher spread0.232 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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