Does the content of financial literacy education resources vary based on who made or paid for them?
Bibliographic record
Abstract
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.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".