Deriving meaning through context: Interpreting bare nominals in second language Japanese
Bibliographic record
Abstract
Previous studies on the second language acquisition of telicity have suggested that learners can use morphosyntactic cues to interpret sentences as telic or atelic even in cases where the cues differ in the first language (L1) and second language (L2) (Slabakova, 2001, 2005; Gabriele, 2008; Kaku et al., 2008a, 2008b). The present study extends this line of research by focusing on a case in which learners cannot rely on morphosyntactic cues in order to reach the appropriate aspectual interpretation. We examine the acquisition of telicity by English-speaking learners of Japanese, focusing on how learners interpret bare count nouns such as kaado ‘card’ that obligatorily display count noun morphosyntax in English. In Japanese, a bare noun such as kaado is ambiguous with respect to number and therefore a verb phrase such as kaado-o kakimashita ‘wrote card’ can be interpreted as either telic ‘wrote the cards’ or atelic ‘wrote cards’ depending on the context. The results of two studies with both intermediate (Study 1: n = 38; Study 2: n = 38) and advanced (Study 1: n = 7; Study 2: n = 10) learners of Japanese show that there are learners at both levels of proficiency that have difficulty with the interpretation of bare count nouns and assign an exclusively telic reading to a verb phrase such as kaado-o kakimashita ‘wrote card’. We argue that this interpretation is due to the boundedness of count nouns in L1 English and propose that a retreat from negative transfer is difficult when there is variability in the native speaker input and when meaning has to be derived from context in the absence of morphosyntactic cues.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".