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Record W4291363151 · doi:10.30862/jhm.v5i2.261

Understanding our world in a time of crisis: Mathematics education pedagogy toward financial numeracy

2022· article· en· W4291363151 on OpenAlex
Alexandre Cavalcante, Annie Savard

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

VenueJournal of Honai Math · 2022
Typearticle
Languageen
FieldMathematics
TopicStatistics Education and Methodologies
Canadian institutionsMcGill UniversityUniversity of Toronto
Fundersnot available
KeywordsNumeracyFinancial crisisDimension (graph theory)Mathematics educationFinancePedagogyMathematicsPsychologyBusinessEconomics

Abstract

fetched live from OpenAlex

This paper aims to address some implications for mathematics education regarding the financial and economic implications of the beginning of the COVID-19 pandemic. We use the term financial numeracy to refer to the quantitative aspect of financial education while also arguing for it to be considered a domain of mathematics education. Financial numeracy entails three dimensions: contextual, conceptual, and systemic. We bring three examples of financial implications of the crisis in different countries. Based on these examples, we constructed learning situations that reflect the distinct orientations of each dimension of financial numeracy to clarify the teaching of such a concept in school mathematics. Particularly in a time of crisis, mathematics education must address immediate needs of society as well as contribute to overcoming social challenges. We hope that financial numeracy brings innovative solutions to teach mathematics in a way that helps individuals and communities produce and manage resources while protecting the planet.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.426

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.313
GPT teacher head0.459
Teacher spread0.146 · 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