EVIDENCE OF LEXICAL TRANSFER IN LEARNER SYNTAX
Why this work is in the frame
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Bibliographic record
Abstract
This article reports the findings of a study in which transfer of verb properties was investigated via syntactic data elicited from second language (L2) learners. It was hypothesized that a learner's first language (L1) would influence the acquisition of verbs in those L2 semantic classes where so-called L1-L2 translation equivalents could be found. To investigate lexical transfer, the performance of Hindi-Urdu speakers on tests of English causatives was compared with that of Vietnamese speakers, because there are significant differences between causativization patterns in Hindi-Urdu and Vietnamese. To account for proficiency-based variation in performance, learners were placed in one of three levels of lexical proficiency in English, and Mann-Whitney comparisons were made between Hindi-Urdu and Vietnamese speakers at corresponding proficiency levels. It was found that the performance of the Hindi-Urdu and Vietnamese groups differed significantly in several semantic contexts. Generally, the results suggest that there is some transfer of semantic information from the L1 verb lexicon to the emerging L2 verb lexicon. More specifically, the findings suggest that verb properties are transferred selectively and that transfer plays a role in the difficulty or ease involved in the shedding of overgeneralized lexical rules.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.096 | 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 it