Three yellow stars and three red hearts: Can subset-knowers learn number word meanings from multiple exemplars?
Bibliographic record
Abstract
Numerous studies have shown that number word learning is a protracted process. One challenge facing children learning the meaning of number word such as “one”, “two”, or “three” is that number words refer to a property of a set and not to individual objects. In this study, we focused on a sample of children who have not learned the meaning of small number words such as “two” and “three” and tested whether children could learn number words from examples of sets that help them focus on set size. Specifically, the experimental training condition included examples that highlight a common relational structure between sets through varying object properties in the sets (e.g., three yellow stars and three red hearts are both “three”), whereas the control condition did not vary object properties(e.g., two sets of three yellow stars with different spatial arrangement). We trained two- and three-knowers (N = 65) on the next number (i.e., three or four) and assessed their learning with a Two-Alternative-Forced-Choice task and Give-a-Number task. Overall, we found weak effects of training. We discuss our findings in the broader literature on number word learning and explore the possibility of analogical reasoning as a mechanism of number word learning.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".