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ANÁLISE PRAXEOLÓGICA DE DIRETRIZES CURRICULARES REFERENTE AO ESTUDO DE NÚMEROS BINÁRIOS

2019· article· pt· W2943369727 on OpenAlexaboutno aff
Herman do Lago Mendes

Bibliographic record

VenueREAMEC - Rede Amazônica de Educação em Ciências e Matemática · 2019
Typearticle
Languagept
FieldSocial Sciences
TopicEducation Pedagogy and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsBinHumanitiesPhilosophyComputer scienceProgramming language

Abstract

fetched live from OpenAlex

Objetiva investigar a abordagem de números binários, a nível escolar, em diretrizes curriculares de Matemática e de Computação. Para tal, recorre à Teoria Antropológico do Didático como elemento teórico e, mais especificamente, a praxeologia como ferramenta de pesquisa. Não identifica abordagem de números binários em diretrizes curriculares internacionais de Matemática: Ontario (2005, 2007). No entanto, identifica abordagem de números binários em diretrizes curriculares internacionais de Computação: Ontario (2008, 2009) e Computer Science Teachers Association (2011). Os números binários configuram-se como tema ou setor de estudo necessário para a compreensão de outros saberes, próprio, da Computação. Identificamos dezesseis tipos de tarefas, duas técnicas e princípios de tecnologia nessas diretrizes curriculares internacionais de Computação.

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)0.008
metaresearch head score (Gemma)0.063
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: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.063
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.011
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.002

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 head0.039
GPT teacher head0.361
Teacher spread0.322 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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