Bibliographic record
Abstract
How do children learn to interpret structurally complex noun phrases? NPs embedded inside other NPs are not accessible to predication, so that in a sentence with a subject NP containing a PP modifier such as The cup on the table is green or The dog with the bone is blue, the adjectival predicate has scope over the highest but not the embedded nominal referent (Arsenijevic & Hinzen 2012). We used a coloring task to examine children’s comprehension of sentences containing these complex NPs, comparing PP modifiers (locative and comitatives) to coordinated NPs (The cup and the table are green), where both referents are accessible. Three- to five-year-old children were highly accurate with control and coordinate sentences, and performed well with locative PPs, but were not different from chance level for comitative sentences, which many children treated as coordinates. That children differentiate between coordinate and locative sentences provides evidence that children have early access to the syntax-semantics of complex nominals. The contrast between locatives and comitatives suggests that comprehension is not merely guided by subject agreement (since the agreement patterns are the same for both types of PP-modified subjects), and that children still need to learn the lexical semantics of prepositions. Diachronically, languages with comitative modifiers evolve into language with comitative coordination (Haspelmath 2007). Thus, we propose that these error patterns for comitative prepositions can be explained by the assumption that children’s errors align with the direction of systematic language change.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".