What Do Children with Above-Average Abilities Understand About Financial Literacy?
Bibliographic record
Abstract
Metaphors help to simplify complex concepts, making them more accessible and understandable for children. Children can build a more concrete understanding of these concepts by associating abstract financial ideas with familiar situations or objects. The present study aims to explore what children with above-average abilities understand by financial literacy, using words and images as tools of expression. During a workshop, 22 children with above-average abilities participated in two tasks, one individual and one group task. The results showed that “save” (90.9%), “money” (63.9%), “invest” (59.1%), and “bank” (54.5%) are the words most strongly associated with the concept of financial literacy among the children. Regarding images, money (M = 1.77), a clock or calendar (M = 2.50), a pig (M = 2.75), and a house (M = 2.84) were identified as the most representative symbols of financial literacy for this group of children. In the group task, children perceive financial literacy mainly as managing and using money to satisfy needs and desires. The results can inform educators about the need to adapt educational materials to match children’s level of understanding better, promoting more effective and accessible financial education.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".