Children's Interpretation of Indefinites in Sentences Containing Negation: A Reassessment of the Cross-linguistic Picture
Bibliographic record
Abstract
Previous research suggests that children's behavior with respect to the interpretation of indefinite objects in negative sentences may differ depending on the target language: whereas young English-speaking children tend to select a surface scope interpretation (e.g., Musolino (1998) Musolino, J. 1998. Universal Grammar and the Acquisition of Semantic Knowledge: An Experimental Investigation Into the Acquisition of Quantifier-Negation Interaction in English, PhD Dissertation University of Maryland. [Google Scholar]), young Dutch-speaking children consistently prefer an inverse scope interpretation (e.g., Kämer (2000) Krämer, I. 2000. Interpreting Indefinites, PhD Dissertation Utrecht, , The Netherlands: Utrecht University. [Google Scholar]). In this article, we suggest that these data are not as puzzling as they first appear. Extending a proposal put forward by Hulsey, Hacquard, Fox, and Gualmini (2004) Gualmini, A. 2004. The Ups and Downs of Child Language: Experimental Studies in Children's Knowledge of Entailment Relationships and Polarity Phenomena Routledge, NY [Google Scholar], we show that both English- and Dutch-speaking children's behavior can be explained in the same way: children select the interpretation that answers the contextually relevant question.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".