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Um panorama de conceitos financeiros nos currículos canadenses: o caso de juros simples e compostos

2025· article· pt· W4411877948 on OpenAlex
Alexandre Cavalcante

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEm Teia | Revista de Educação Matemática e Tecnológica Iberoamericana · 2025
Typearticle
Languagept
FieldBusiness, Management and Accounting
TopicFinancial Reporting and Valuation Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPanoramaHumanitiesComputer sciencePhilosophyArtificial intelligence

Abstract

fetched live from OpenAlex

Neste artigo, exploramos as maneiras pelas quais juros simples e compostos (referidos no artigo pelo acrônimo inglês SCI) são incorporados à matemática do ensino médio nos programas curriculares das províncias do Canadá. A análise comparativa de documentos de todos os dez currículos provinciais revelou uma miríade de abordagens que propõem que o SCI seja ensinado da 5ª à 12ª série para diferentes perfis de alunos (acadêmicos, não acadêmicos) em múltiplas vertentes matemáticas: aritmética (número), álgebra (discreto e funções exponenciais), dados e probabilidade, educação financeira, matemática financeira e vertentes específicas de finanças (banco, juros e crédito, gestão de dinheiro, etc.). No geral, essa inconsistência na integração do JSC no Canadá é provavelmente o resultado da falta de consenso na pesquisa em educação matemática em relação à alfabetização financeira e seu papel na disciplina de matemática.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.004
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.026
GPT teacher head0.320
Teacher spread0.294 · 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