One Size Does Not Fit All: Conceptual Concerns and Moral Imperatives Surrounding Gender-Inclusive Financial Literacy Education
Bibliographic record
Abstract
In the wake of the 2008 global economic crisis, financial literacy education received increased political attention worldwide as an important policy solution to achieve a variety of ends. Cloaked in the neoliberal language of value-neutrality, financial literacy education takes on a gender-blind character, presuming a level playing field. Through its naivety, financial literacy education perpetuates the false impression that men and women experience economic participation, decisions and outcomes in the same ways. This article explores how attention to gender justice is an important moral obligation if we are to achieve inclusive financial literacy education and recommends feminist pedagogies to counter dominant and uncritical approaches to financial literacy in classrooms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.122 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".