Bibliographic record
Abstract
In psycholinguistic research the exact level of language selection in bilingual lexical access is still controversial and current models of bilingual speech production offer conflicting statements about the mechanisms and location of language selection. This paper aims to provide a corpus analysis of self-repair mechanisms in code-switching contexts of highly fluent bilingual speakers in order to gain further insights into bilingual speech production. The present paper follows the assumptions of the Selection by Proficiency model, which claims that language proficiency and lexical robustness determine the mechanism and level of language selection. In accordance with this hypothesis, highly fluent bilinguals select languages at a prelexical level, which should influence the occurrence of self-repairs in bilingual speech. A corpus of natural speech data of highly fluent and balanced bilingual French-English speakers of the Canadian French variety Franco-Manitoban serves as the basis for a detailed analysis of different self-repair mechanisms in code-switching environments. Although the speech data contain a large amount of code-switching, results reveal that only a few speech errors and self-repairs occur in direct code-switching environments. A detailed analysis of the respective starting point of code-switching and the different repair mechanisms supports the hypothesis that highly proficient bilinguals do not select languages at the lexical level.
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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 | 0.004 |
| 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 | 0.002 |
| 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".