Why is number word learning hard? Evidence from bilingual learners.
Bibliographic record
Abstract
Young children typically take between 18 months and 2 years to learn themeanings of number words. In the present study, we investigated thisdevelopmental trajectory in bilingual preschoolers to examine the relativecontributions of two factors in number word learning: (1) the constructionof numerical concepts, and (2) the mapping of language specific words ontothese concepts. We found that children learn the meanings of small numberwords (i.e., one, two, and three) independently in each language,indicating that observed delays in learning these words are attributable todifficulties in mapping words to concepts. In contrast, children generallylearned to accurately count larger sets (i.e., five or greater)simultaneously in their two languages, suggesting that the difficulty inlearning to count is not tied to a specific language. We also replicatedprevious studies that found that children learn the counting procedurebefore they learn its logic – i.e., that for any natural number, n, thesuccessor of n in the count list denotes the cardinality n+1. Consistentwith past studies, we find that knowledge of this successor principleexhibits partial transfer between languages, suggesting that the logic ofthe positive integers may not be stored in a language-specific format. Weconclude that delays in learning the meanings of small number word aremainly due to language-specific processes of mapping words to concepts,whereas the logic and procedures of counting appear to be learned in aformat that is independent of a particular language and thus transfersrapidly from one language to the other in development.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".