General and Specific Effects of Lexicon in Grammar: Determiner and Object Pronoun Omissions in Child Spanish
Bibliographic record
Abstract
PURPOSE: This study explores the hypothesis that vocabulary growth can have 2 types of effects in morphosyntactic development. One is a general effect, where vocabulary growth globally determines utterance complexity, defined in terms of sentence length and rates of subordination. There are also specific effects, where vocabulary size has a selective impact on the acquisition of grammatical markers and where lexicon is a prerequisite for typological convergence. The study compares the differential effects of vocabulary in 2 measures of morphosyntactic development: omissions of object clitic pronouns and definite articles. METHOD: Correlation analysis and structural equation models were used to analyze the statistical effects of measures of vocabulary and grammatical development in 110 Spanish-speaking monolingual children ages 3-5 years. RESULTS: The data revealed general effects of vocabulary growth on utterance length and subordination rates and on the use of definite determiners and object pronouns. Specific effects of vocabulary growth were identified for object pronouns but not for determiners. CONCLUSIONS: The study found support for a 2-dimensional model separating lexicon and syntax and for 2 types of relationships. Vocabulary development generally determines sentence complexity and further evidence for specific effects in object pronoun use.
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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 | 0.009 |
| 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".