The future is now: Age-progressed images motivate community college students to prepare for their financial futures.
Bibliographic record
Abstract
Part of the challenge young people face when preparing for lifelong financial security is visualizing the far-off future. Age-progression technology has been shown to motivate young people to save for retirement. The current study applied age progression for motivating socioeconomically diverse community college students as part of a college planning course. We recruited 106 students enrolled in a mandatory "Transitioning to College" course and randomly assigned them to view age-progressed or same-aged digital avatars. Compared to controls, age-progressed participants gave more correct answers and exhibited higher confidence (i.e., fewer "don't know" responses) on a financial literacy test. Confidence mediated the effect of age progression on correct responses, but not the other way around, pointing to financial confidence as a precursor to effective financial education. Students also reported interest in attending more long-term financial planning workshops (e.g., investing and retirement) available through their college. No differences were observed in financial planning for the near term (e.g., student aid and credit cards). The current study demonstrates the viability of age progression as a practical, inexpensive, and scalable intervention. Findings also illustrate the significance of this intervention for reducing pervasive socioeconomic and age disparities in financial knowledge and enhancing long-term financial prospects across future generations. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".