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The Teaching of Fractions – Emerging Questions from the Combined Reading of Brazilian and Canadian Curricular Documents

2021· article· en· W3193590366 on OpenAlexaffabout
Priscila Dias Corrêa, Letícia Rangel

Bibliographic record

VenueInternational Journal for Cross-Disciplinary Subjects in Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMathematics Education and Teaching Techniques
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsReading (process)Mathematics educationLibrary sciencePsychologyPedagogySociologyComputer scienceLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

To contribute to the reflective process inherent to the teaching practice and to the development of knowledge of mathematics for teaching, this article analyzes curriculum documents from two different countries and offers some questions for discussion.The focus of the study is on the teaching of numbers and the analysis emphasizes fractions.The article is based on an analytical study on the teaching of numbers based on the combined reading of official curricula from Brazil and Canada.It contributes to establishing a methodology of relational analysis among curricular documents.The study offers a visual model, the curricular trajectory, which points out parities and contrasts grounded on the identification of elementary aspects revealed in the structuring elements of both curricula.Based on the curricular trajectory, this paper poses questions of fundamental relevance to the teaching of fractions.

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)0.005
metaresearch head score (Gemma)0.018
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 score0.115
Threshold uncertainty score0.307

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.007
Science and technology studies0.0080.011
Scholarly communication0.0070.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.431
Teacher spread0.412 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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