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Record W3089450921 · doi:10.1177/2047173420961031

Does the content of financial literacy education resources vary based on who made or paid for them?

2020· article· en· W3089450921 on OpenAlexaffabout
Gail E. Henderson, Pamela Beach, Lucy Sun, Jen McConnel

Bibliographic record

VenueCitizenship Social and Economics Education · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsQueen's University
Fundersnot available
KeywordsFinancial literacyFinancial servicesGovernment (linguistics)CurriculumBusinessFinanceProfit (economics)LiteracyFinancial crisisPublic relationsMarketingEconomicsPolitical scienceEconomic growth

Abstract

fetched live from OpenAlex

In the decade since the global financial crisis, an increasing number of jurisdictions have added mandatory financial literacy education to school curricula. Governments recognize that this increases the burden on teachers, who may also lack the confidence to teach financial literacy. One response is to encourage the use of resources produced or sponsored by the financial services industry. The concern is that these resources may promote the industry’s interest in maximizing profits and minimizing regulation over students’ interest in becoming empowered financial consumers. As a first step in investigating this concern, we compared resources from the Canadian Financial Literacy Database produced or sponsored by the financial services industry with those produced by government, non-profit organizations and individuals. We focused on online resources intended for use by elementary teachers and students to determine whether the key themes and messages conveyed vary based on who made or paid for the resource. We found that key themes are consistent across resources, regardless of industry affiliation, but that resources produced or sponsored by the financial services industry are more likely to exhibit a moralistic tone.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.056
GPT teacher head0.253
Teacher spread0.197 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2020
Admission routes2
Has abstractyes

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