The acquisition of argument structures of intransitive and transitive verbs in Japanese: The role of parental input
Bibliographic record
Abstract
The present study investigated the role of morphosyntactic information in the acquisition of transitive and intransitive verb argument structures (VAS) in the Japanese language, which allows massive omissions of arguments and case markers. In particular, we investigated how the ‘variation sets’ proposed by Küntay and Slobin work in Japanese. Longitudinal interaction data from three Japanese-speaking mother–child pairs were collected at five different times between the ages of 0;10 and 3;01. Children’s acquisition of VAS and mothers’ use of verbs were examined, including morphologically related verbs in a variety of sentence frames with null and overt arguments. The results indicate that all three mothers showed an increase in overt arguments in different syntactic roles as well as lexical given arguments around the time that children started uttering words. However, the use of a variety of sentence frames with null and overt arguments was not uniform among the mothers, and such individual differences were related to the acquisition of VAS among children. These findings support the role of ‘variation sets’ in the acquisition of VAS in Japanese and suggest that the availability of morphosyntactic information in the input helps children to reconstruct VAS.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".