Children’s Early Acquisition of Syntactic Category: A Corpus-Based Analysis of English Determiner-Noun Combinational Flexibility
Bibliographic record
Abstract
While the generativist account posits that an abstract specification of syntactic categories is innate and children show adult-like performance from an early stage, the constructivist account postulates that children’s early acquisition of grammatical categories is item-based and reflects limited rules later. The present study tests these assumptions in a specific category, the English determiners. More specifically, we took the controlled measures of overlap (e.g., the use of definite article the and indefinite articles a/an before the same noun type) in 16 children and their mothers’ spontaneous speech as an indicator of determiner-noun combinational flexibility. A series of three studies were conducted, in which we strictly controlled the impact of differences between children and adults in lexical knowledge. In Study 1 and Study 2, we find that children’s use of determiners shows a significant difference from adults but this difference disappeared later. Furthermore, Study 3 investigates the influence of external environment with birth order and family’s social class as factors and emphasizes that the input factor is worthy of further investigation in future studies. These findings are consistent with one of the constructivist claims, namely that children’s early acquisition of determiners is not category-based and their flexibility in using determiners gradually approximates that of adults with 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".