Why are Noun-Verb-<i>er</i> compounds so difficult for English-speaking children?
Bibliographic record
Abstract
Preschool children who attempt novel NV-er compounds (like cat brusher) often misorder the noun and the verb, arguably based on sentential phrasal ordering (e.g., Clark, Hecht, & Mulford, 1986). In this study, we test this argument by replicating Clark’s prediction that children’s attempts will fall into predictable stages based on age and by comparing children’s production of NV-er compounds with another construction that violates sentential phrasal ordering: Verb-ingNoun phrases. Our studies show that we could not replicate the stages described by Clark and that children were more likely to produce Verb-ingNoun constructions in the target order than NV-er. However, the children’s constructions showed a contingency between the order of the elements and the children’s choice of morpheme, suggesting that they were often aiming for the target form. These results suggest that children do not misorder nouns and verbs in NV-er compounds because of phrasal ordering. We discuss possible alternatives for why NV-er compounds are difficult for preschool 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| 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".