The Role of Knowledge Brokers in Improving Financial Literacy
Bibliographic record
Abstract
Several governments around the world have tried strategies based primarily on financial education programs to improve the financial literacy of their citizens. In this study, we discuss a new strategy that involves using knowledge transfer activities carried out by intermediary agents, called financial knowledge brokers, to achieve significant improvement in financial literacy. Thus, the aim of this paper is to test the impact of the five activities of financial knowledge brokers (i.e., financial knowledge acquisition, financial knowledge integration, financial knowledge adaptation, financial knowledge dissemination, and creation of links) on financial literacy. For this, we built a database from a questionnaire carried out to nearly 103 financial advisers during the period June 2015 to June 2017. Overall, the results of Structural equation Modeling (SEM) technique showed that the financial knowledge brokerage activities (four of the five activities) have a positive impact on improving financial literacy as well as on its four dimensions, namely financial attitude, financial behavior, basic financial knowledge, and advanced financial knowledge. JEL classification numbers: D80, F65, G20, I20. Keywords: Financial literacy, Knowledge brokers, Structural equation modeling.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".