Bibliographic record
Abstract
This paper presents a study that attributes verb serialization in the interlanguage of Vietnamese-speaking ESL learners to language transfer and, furthermore, puts forward the view that such transfer bears a resemblance to substrate influence in creoles with serial verb constructions (SVCs). In a task that elicited English causatives through pictures representing the causation of events, a subset of the Vietnamese-speaking participants in this study produced a number of serial-type constructions that reflected lexicosemantic aspects of causative SVCs in Vietnamese. Speakers of Hindi-Urdu, a nonserializing language used for comparative purposes, did not produce any equivalents. Additionally, serial-type constructions with second verbs (V2s) representing a result (e.g., cook butter melt) predominated at lower levels of lexical proficiency, whereas serials with make and a result (e.g., make broken) were more evenly distributed across proficiency levels. One inference based on the results is that certain serials are eliminated early in the acquisition process through positive evidence obtained via English input, whereas others continue to appear beyond the elementary level because of misleadingly similar constructions in the input. A comparison of the proficiency-based transfer of “cook butter melt” serials in this study and the inferred transfer of SVCs in creolization suggests that, whereas transfer processes in the two contexts are congruent in certain ways (often resulting from the exigencies of communication, limited access to the TL, and linguistic convergence), the processes diverge because of differences in target norms and input conditions. The latter two factors provide one explanation for why SVC-related transfer effects were limited to a subgroup of Vietnamese-speaking participants in this study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".