Finding one’s meaning: A test of the relation between quantifiers and integers in language development
Bibliographic record
Abstract
We explored children’s early interpretation of numerals and linguisticnumber marking, in order to test the hypothesis (e.g., Carey, 2004) thatchildren’s initial distinction between one and other numerals (i.e., two,three, etc.) is bootstrapped from a prior distinction between singular andplural nouns. Previous studies have presented evidence that in languageswithout singular- plural morphology, like Japanese and Chinese, childrenacquire the meaning of the word one later than in singular-plural languageslike English and Russian. In two experiments, we sought to corroborate thisrelation between grammatical number and integer acquisition within English.We found a significant correlation between children’s comprehension ofnumerals and a large set of natural language quantifiers and determiners,even when controlling for effects due to age. However, we also found that2-year-old children, who are just acquiring singular-plural morphology andthe word one, fail to assign an exact interpretation to singular nounphrases (e.g., a banana), despite interpreting one as exact. For example,in a truth value judgment task, most children judged that a banana wasconsistent with a set of two objects, despite rejecting sets of two for thenumeral one. Also, children who gave exactly one object for singular nounsdid not have a better comprehension of numerals relative to children whodid not give exactly one. Thus, we conclude that the correlation betweenquantifier comprehension and numeral comprehension in children of this ageis not attributable to the singular-plural distinction facilitating theacquisition of the word one. We argue that quantifiers play a more generalrole in highlighting the semantic function of numerals, and that childrendistinguish between numerals and other quantifiers from the beginning,assigning exact interpretations only to numerals.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".