Interpreter-mediated interaction as bilingual speech: Bridging macro- and micro-sociolinguistics in codeswitching research
Bibliographic record
Abstract
This article investigates codeswitching, codemixing, and other bilingual speech phenomena in interpreter-mediated interaction, a type of data that has been largely ignored by linguists working on bilingualism. It is argued that important theoretical considerations exist for considering such data, as it represents interaction between speakers of the different languages that are in contact (exolingual interaction) and thus enables the researcher to link micro-sociolinguistic observations about the interaction to macro-sociolinguistic facts of the contact situation more generally. Investigating data from arbitration hearings in New York City courts during which speakers of Haitian Creole, Polish, Russian, or Spanish interact with English speakers, it is shown that bilingual speech phenomena like codeswitching to English and insertion of English lexical items in other language structures pattern in ways that parallel the findings of more traditional studies that draw on in-group interaction. However, it is argued that their asymmetrical distribution can be directly related to the power asymmetries that hold between English-speaking court officials and other-language-speaking court users. Furthermore, it is shown that investigating interpreter-mediated interaction has several methodological advantages, as it facilitates a cross-linguistic comparison across parallel interactional episodes and avoids several problems of researcher access and observer effects that often constrain codeswitching studies.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".