Older and Younger Adults ' Strategy Use and Execution in Currency Conversion Tasks: Insights From French Franc to Euro and Euro
Bibliographic record
Abstract
Younger and older adults were taught new strategies for converting amounts presented in French francs into euros or amounts presented in euros into French francs. The choice/no-choice method was used to obtain information on how often each newly learned strategy was used as well as information on the speed and accuracy of strategies. The results showed that both younger and older participants used the new conversion strategies unequally often and had strategy preferences that were justified by the relative ease of execution of each strategy. We discuss numerous practical applications of the present findings, as they suggest that one can help younger and older people by teaching them the add-half and divide-three strategies for the French-franc-euro conversions, that no specific strategies should be taught to older people, and that newly taught strategies are more efficient than those people use spontaneously. Suppose you are on vacation in France for the first time and are about to buy your first real French baguette. The baker tells you that it costs 4 francs and 35 centimes. How much is that in U.S. or Canadian dollars? How can you find out? You can (a) use your calculator and do the conversion, knowing that U.S. $1 is 7.11 French francs (FF), (b) choose not to do the conversion, or (c) do
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".