On the relation between the acquisition of singular–plural morpho‐syntax and the conceptual distinction between one and more than one
Bibliographic record
Abstract
We investigated the relationship between the acquisition of singular-plural morpho-syntax and children's representation of the distinction between singular and plural sets. Experiment 1 tested 18-month-olds using the manual-search paradigm and found that, like 14-month-olds (Feigenson & Carey, 2005), they distinguished three objects from one but not four objects from one. Thus, they failed to represent four objects as 'plural' or 'more than one'. Experiment 2 found that children continued to fail at the 1 vs. 4 manual-search task at 20 months of age, even when told, via explicit morpho-syntactic singular-plural cues, that one or many balls are being hidden. However, 22- and 24-month-olds succeeded both with and without verbal cues. Parental report data indicated that most 22- and 24-month-olds, but few 20-month-olds, had begun producing plural nouns in their speech. Also, the success among the older children was due to those children who had reportedly begun producing plural nouns. We discuss a possible role for language acquisition in children's deployment of set-based quantification and the distinction between singular and plural sets.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".