Revisiting (Non-)Native Influence in VOT Production: Insights from Advanced L3 Spanish
Bibliographic record
Abstract
A growing body of research investigating cross-linguistic influence on the acquisition of a third phonological system suggests that first (L1) and second (L2) languages concur in influencing oral production in the target third language (L3). Yet, there are also claims of either a more noticeable effect of the L2 on the L3, or a prevailing influence from the L1. This study further explores whether the L1 and the L2 compete or converge on exerting influence on L3 pronunciation. To do so, we examine the production of voice onset time for voiceless stops by adult advanced learners of L3 Spanish divided into two groups (15 L1 English-L2 French, and 15 L1 French-L2 English speakers). Three monolingual control groups were also tested. Participants were recorded reading word lists that contained voiceless stops in stressed onset position. A Kruskal-Wallis test uncovered significant differences traceable to the L1-English speakers, which puts them at a slight disadvantage vis-à-vis their Francophone counterparts. These results favor claims of a more decisive role for the L1 in L3 pronunciation. We compare our results to findings from previous studies targeting intermediate learners, and find proficiency in the L3 may account for the observed differences.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".