Strategic flexibility in computational estimation for Chinese- and Canadian-educated adults.
Bibliographic record
Abstract
The purpose of the present study was to examine factors that influence strategic flexibility in computational estimation for Chinese- and Canadian-educated adults. Strategic flexibility was operationalized as the percentage of trials on which participants chose the problem-based procedure that best balanced proximity to the correct answer with simplification of the required calculation. For example, on 42 × 57, the optimal problem-based solution is 40 × 60 because 2,400 is closer to the exact answer 2,394 than is 40 × 50 or 50 × 60. In Experiment 1 (n = 50), where participants had free choice of estimation procedures, Chinese-educated participants were more likely to choose the optimal problem-based procedure (80% of trials) than Canadian-educated participants (50%). In Experiment 2 (n = 48), participants had to choose 1 of 3 solution procedures. They showed moderate strategic flexibility that was equal across groups (60%). In Experiment 3 (n = 50), participants were given the same 3 procedure choices as in Experiment 2 but different instructions and explicit feedback. When instructed to respond quickly, both groups showed moderate strategic flexibility as in Experiment 2 (60%). When instructed to respond as accurately as possible or to balance speed and accuracy, they showed very high strategic flexibility (greater than 90%). These findings suggest that solvers will show very different levels of strategic flexibility in response to instructions, feedback, and problem characteristics and that these factors interact with individual differences (e.g., arithmetic skills, nationality) to produce variable response patterns.
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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 | 0.010 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".