Factors that Influence Children's Acquisition of Adjective-Noun Order
Bibliographic record
Abstract
Usage-Based theoreticians have argued that children make the biggest strides in learning to use many adult-like grammatical rules in the preschool years.This argument is based on how children use novel verbs in verb clauses: many Englishspeaking 2-year olds are willing to use novel verbs in ungrammatical order; by 4, few children are willing to use novel verbs in a non-SVO order.In verb clauses, the word order determines the semantic/syntactic role (e.g., subject).By focusing on verbs, researchers have failed to take into account that children might also be learning how meaning and semantic/syntactic function are related.To test this interpretation, we taught novel adjectives to 35 monolingual English-speaking children between 2 and 4 years old, either in a prenominal or postnominal position.Results showed that, while children were more likely to reverse the order of novel postnominal adjectives, even 4-year olds used the new adjectives in the order they were modeled more than half the time.These results suggest that during the preschool years, children are learning to map word order onto semantic/syntactic function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".