Codeswitching: A Bilingual Toolkit for Opportunistic Speech Planning
Bibliographic record
Abstract
The ability to engage in fluent codeswitching is a hallmark of the flexibility and creativity of bilingual language use. Recent discoveries have changed the way we think about codeswitching and its implications for language processing and language control. One is that codeswitching is not haphazard, but subject to unique linguistic and cognitive constraints. Another is that not all bilinguals codeswitch, but those who do, exhibit usage patterns conforming to community-based norms. However, less is known about the cognitive processes that regulate and promote the likelihood of codeswitched speech. We review recent empirical studies and provide corpus evidence that highlight how codeswitching serves as an opportunistic strategy for optimizing performance in cooperative communication. From this perspective, codeswitching is part and parcel of a toolkit available to bilingual codeswitching speakers to assist in language production by allowing both languages to remain active and accessible, and therefore providing an alternative means to convey meaning, with implications for bilingual speech planning and language control more generally.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".