Triggered codeswitching: Lexical processing and conversational dynamics
Bibliographic record
Abstract
Abstract This study investigates the psycholinguistic process underlying triggered codeswitching – codeswitching facilitated by the occurrence of cognates – within the context of conversational dynamics. It confirms that, in natural bilingual speech, lexical selection of cognates can facilitate codeswitching by enhancing the activation of the non-selected language. Analyses of a large-scale corpus of Welsh–English conversational speech showed that 1) producing cognates facilitated codeswitching, 2) speakers who generally produced more cognates generally codeswitched more, even in clauses that did not contain cognates, 3) larger numbers of cognates in a clause increased the likelihood of codeswitching, 4) codeswitching temporarily remained facilitated after the production of cognates, and 5) hearing rather than producing cognates did not facilitate codeswitching. The findings confirm the validity of the proposed cognitive account of triggered codeswitching, and clarify the relation between the lexical activation of cognates and consecutive language choice, in accord with current insights in lexical processing.
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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 | 0.000 | 0.005 |
| 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) | 0.002 | 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".