Continuity in lexical and morphological development in Icelandic and English-speaking 2-year-olds
Bibliographic record
Abstract
Accounts of language development vary in whether they view lexical and grammatical development as being mediated by a single or by separate mechanisms. In a single mechanism account, only one system is required for learning words and extracting grammatical regularity based on similarities among stored items. A strong non-linear relationship between early lexical and grammatical development has been demonstrated in English and, more recently, in Italian supporting a single mechanism view (Caselli, Casadio & Bates 1999, Marchman & Bates 1994). The present study showed a comparable non-linear relationship between vocabulary size and the emergence of verb inflection and sentence complexity in two-year-old speakers of English and Icelandic, a highly inflected language. The study included 96 children within a narrow age range, but varying extensively in language proficiency, demonstrating continuity in lexical and grammatical development among children with typical language development as well as very precocious children and children with expressive language delay. Cross-linguistic differences were noted as well, suggesting that the Icelandic-speaking children required a larger critical mass of vocabulary items before grammatical regularity was detected. This is probably a result of the more complex inflectional system of the Icelandic language compared with English.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".