NATIVELIKE BIASES IN GENERATION OF <i>Wh</i> -QUESTIONS BY NONNATIVE SPEAKERS OF JAPANESE
Bibliographic record
Abstract
A number of studies of second language (L2) sentence processing have investigated whether ambiguity resolution biases in the native language (L1) transfer to superficially similar cognate structures in the L2. When transfer effects are found in such cases, it is difficult to determine whether they reflect surface parallels between the languages or the operation of more abstract processing mechanisms. Wh -questions in English and Japanese present a valuable test case for investigating the relation between L1 and L2 sentence processing. Native speakers (NSs) of English and Japanese both show strong locality biases in processing wh -questions, but these locality biases are realized in rather different ways in the two languages, due to differences in word order and scope marking. Results from a sentence generation study with NSs of Japanese and advanced English-speaking L2 learners of Japanese show that the L2 learners show a strongly nativelike locality bias in the resolution of scope ambiguities for in situ wh -phrases, despite the fact that the closest analogue of such an interpretation is impossible in English. This indicates that L2 learners are guided by abstract processing mechanisms and not just by superficial transfer from the L1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".