More than one way to free choice: A view from child Romanian
Bibliographic record
Abstract
Studies show that children draw from modalized disjunctive statements of the structure X is allowed to do P or Q (♢(P ∨Q)) a Free Choice (FC) inference, namely X is allowed to do P and X is allowed to do Q (♢P ∧♢Q). Their ability to compute free choice inferences is surprising in light of their well-known difficulties with scalar implicatures involving non modalized disjunction (Tieu, Romoli, et al. 2016), particularly on accounts that unify free choice inferences and scalar implicatures (e.g., Kratzer and Shimoyama 2002; Chierchia 2013). Recent work by Cochard, van Hout, and Demirdache (2024b), however, argues that some children only seemingly derive free choice: these children actually interpret ♢(P ∨Q) as ♢(P ∧Q), which follows from their conjunctive understanding of non-modalized disjunction. In the present study, we extend the investigation by comparing the same children’s performance on non-modalized and modalized utterances in Romanian, an understudied language. Specifically, we tested the same group of 5-year-old monolingual Romanian-speaking children and adult controls, balanced for order. Our findings provide partial evidence for Cochard, van Hout, and Demirdache (2024b)’s hypothesis: some children were inclusive with non-modalized disjunction, and appeared to derive genuine free choice on the free choice task, while some children indeed exhibited conjunctive interpretations in both tasks.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".