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Record W2146087651 · doi:10.3138/topia.30-31.147

Financial Literacy Education as Public Pedagogy for the Capitalist Debt Economy

2014· article· en· W2146087651 on OpenAlexvenueno aff
Chris Arthur

Bibliographic record

VenueTOPIA Canadian Journal of Cultural Studies · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial literacyDebtAusterityFinancializationCapitalismEconomicsFinancePolitical science

Abstract

fetched live from OpenAlex

Financial literacy education often appears as part of a solution to the individualization of economic risk, growing indebtedness, financialization and ongoing austerity. Using concepts from Marx, Žižek and Foucault, this paper analyses a number of prominent debt and investment television shows, arguing that these forms of entertainment work with financial literacy curriculum documents, editorials and speeches to form a public pedagogic dispositif that supports the creation of subjects ethically disposed to recreate the debt economy’s hierarchical relations and practices. In contrast to those who see financial literacy education as a tool for reducing debt and ameliorating financial and economic insecurity, I argue that financial literacy education is better seen as a technology that supports the production of an economic system that produces debt and financial and economic insecurity. To challenge the capitalist debt economy, the paper ends by calling for a critical public pedagogy to create the conditions in which all can be secure and realize their human capacities free from the constraints imposed by capital–conditions we owe others and ourselves a debt to continually recreate.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.918
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.041
GPT teacher head0.286
Teacher spread0.245 · 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 designNot applicable
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

Citations17
Published2014
Admission routes1
Has abstractyes

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