Finding the key in Kiwi during second language spoken production: Low proficiency speakers sound more native-like if they live in mixed-language environments.
Bibliographic record
Abstract
The goal of this article was to determine if second language (L2) speakers benefit from living in mixed-language environments and whether said benefit applies across proficiency levels. To this end, we reanalyzed a subset of data from Gilbert et al. (2019) considering language entropy scores as a proxy for linguistic environment predictability. The task involved producing sentences designed around oronyms in French and English. Participants produced sentences in both languages, allowing the comparison of first language and L2 productions. Their results demonstrated the production of L2-appropriate prosodic cues, albeit after having reached a high level of L2 proficiency. Adding language entropy scores to the original statistical models revealed significant interactions suggesting that participants benefited from living in a mixed-languages environment whereby even low-proficiency speakers produced L2-appropriate prosodic cues. However, low-proficiency L2 speakers living in predictable linguistic environments failed to adapt their prosodic production to their L2, as previously observed. These results suggest that, irrespective of proficiency, the language environment has a significant impact on nonnative language production. This has implications for language development and models of language acquisition. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.006 |
| 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) | 0.004 | 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".