Representations of abstract grammatical feature agreement in young children
Bibliographic record
Abstract
A fundamental question in language acquisition research is whether young children have abstract grammatical representations. We tested this question experimentally. French-learning 30-month-olds were first taught novel word-object pairs in the context of a gender-marked determiner (e.g., un MASC ravole 'a ravole'). Test trials presented the objects side-by-side while one of them was named in new phrases containing other determiners and an adjective (e.g., le MASC joli ravole MASC 'the pretty ravole'). The gender agreement between the new determiner and the non-adjacent noun was manipulated in different test trials (e.g., le MASC __ravole MASC; *la FEM __ravole MASC). We found that online comprehension of the named target was facilitated in gender-matched trials but impeded in gender-mismatched trials. That is, children assigned the determiner genders to the novel nouns during word learning. They then processed the non-adjacent gender agreement between the two categories (Det, Noun) during test. The results demonstrate abstract featural representation and grammatical productivity in young children.
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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 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".