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Record W1481931417 · doi:10.25071/1916-4467.34299

Social Justice and the Gender Politics of Financial Literacy Education

2011· article· en· W1481931417 on OpenAlexaffvenueabout
Laura Elizabeth Pinto, Eliabeth Coulson

Bibliographic record

VenueJournal of the Canadian Association for Curriculum Studies · 2011
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFinancial literacyInjusticePoliticsSociologyEquity (law)LiteracyValue (mathematics)NeutralityCurriculumPublic relationsPolitical scienceEconomicsFinanceLawPedagogy

Abstract

fetched live from OpenAlex

In the wake of the 2008 global financial crisis, financial literacy education received increased political attention worldwide as an important policy solution to achieve a variety of ends. Cloaked in the neo-liberal language of value-neutrality, financial literacy education presumes that individuals on a level playing field become "responsible" and "empowered,” motivated and competent to make financial decisions if given certain tacit knowledge. Through its naïveté, this type of financial literacy discourse perpetuates the false impression that choices, decisions and outcomes are the same for all. In this article, we describe the gender politics of contemporary financial literacy discourse, and analyze how it fails to explore women’s experiences in financial arenas by analyzing three popular Canadian financial literacy education curriculum resources designed for use in K-12 classrooms. We describe how these resources fail to acknowledge gender injustice by presenting content through discourses of “choice” and “value neutrality” that fail to critically examine the underlying assumptions crucial to social justice. By ignoring equity issues, these resources perpetuate inequity and marginalization.

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 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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.346
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.070
GPT teacher head0.413
Teacher spread0.343 · 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 teacher head, not a consensus.

Study designObservational
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

Citations53
Published2011
Admission routes3
Has abstractyes

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