The silent diversion of knowledge: Examining inequality of financial knowledge
Bibliographic record
Abstract
Abstract Scholars and policymakers have long been concerned about the lack of financial knowledge and the socioeconomic disparities in financial knowledge in the United States. The objectives of this study are (1) to assess the extent of income‐related inequality in financial knowledge and how it changed between 2012 and 2018 and (2) to determine what factors explain the income‐related inequality in financial knowledge and the change over time. Using National Financial Capability Study datasets, our study revealed that from 2012 to 2018 there was income‐related inequality in financial knowledge. Financial experience, education, and numeracy were major contributing factors to inequality in financial knowledge, according to decomposition analysis. Income‐related inequality in financial knowledge in the United States decreased between 2012 and 2018, mainly due to an overall change in financial experience. This study provides meaningful insights to policymakers and educators interested in improving financial knowledge in the United States.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".