Financial Literacy Simulation in Managerial Finance: Signing Up for a Mock 401(k)
Bibliographic record
Abstract
Much evidence suggests a growing need for financial literacy as a prerequisite to a lifetime of financial well-being. And while it has been recommended that U.S. colleges and universities implement a mandatory financial literacy course (President’s Advisory Council on Financial Literacy, 2009; U.S. Financial Literacy and Education Commission, 2019), no such requirement has been imposed. Currently, only one quarter of college students report access to financial literacy education (Montalto et al, 2019). Guided by best practices and insights, this paper examines the opportunity for an upper-division core business course to support a focused, financial literacy learning opportunity for upper classmates by employing a simulated retirement plan enrollment. This work contributes to the body of work on financial literacy education and represents an efficient approach to providing relevant education to meet the growing financial literacy needs of college students.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| 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".