Bibliographic record
Abstract
The Pathways to Mathematics model (LeFevre et al., 2010) demonstrated the relations among cognitive precursors and mathematical outcomes in children.In this study, I extended the model to adults and compared the model across cultures.Participants included 71 native English speakers and 71 native Chinese speakers.Mathematical outcomes included calculation, word problems, and a number line task.These were predicted by four pathways: linguistic skill, quantitative knowledge, working memory, and spatial ability.Results showed similarities and differences in the model in relation to children.A major difference in the results for adults compared to children was that linguistic skill did not predict adults' performance on calculation, suggesting that linguistic ability is no longer related to symbolic number system knowledge in adults due to the developed ability of automatized number naming.Culture had a moderating effect on contribution of quantitative knowledge towards number line task performance.Better quantitative knowledge was related to better number line estimation for English speakers, but not for Chinese speakers, suggesting different strategy choices across culture.These findings indicate that the relative contributions of linguistic skill, quantitative knowledge, working memory, and spatial ability vary depending on the demands of specific task, and these contributions are generally universal cross-culturally.Overall, the model showed that math development is componential in both children and adults, and across cultures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".