Development of Abstract Grammatical Categorization in Infants
Bibliographic record
Abstract
This study examined abstract syntactic categorization in infants, using the case of grammatical gender. Ninety-six French-learning 14-, 17-, 20-, and 30-month-olds completed the study. In a preferential looking procedure infants were tested on their generalized knowledge of grammatical gender involving pseudonouns and gender-marking determiners. The pseudonouns were controlled to contain no phonological or acoustical cues to gender. The determiner gender feature was the only information available. During familiarization, some pseudonouns followed a masculine determiner and others a feminine determiner. Test trials presented the same pseudonouns with different determiners in correct (consistent with familiarization gender pairing) versus incorrect gender agreement. Twenty-month-olds showed emerging knowledge of gender categorization and agreement. This knowledge was robust in 30-month-olds. These findings demonstrate that abstract, productive grammatical representations are present early in acquisition.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".