Bilingualism and the Analysis of Talk at Work: Code-Switching as a Resource For the Organization of Action and Interaction
Bibliographic record
Abstract
One major characteristic of bilingualism is the way that speakers deploy resources from what may be recognized as two different languages. The meanings of such code-switching, or the motivations of language alternation in bilingual talk, have been discussed within a variety of theoretical paradigms. Whereas the ‘allocational’ paradigm represented by Fishman’s domain analysis sees social structure as determining language choices, the ‘interactional’ paradigm introduced by Gumperz sees these choices as a way of locally achieving a specifi c interactional order (Wei 2005: 376). Within the latter paradigm, conversation analysis (CA) takes a specifi c stance, stressing the importance of the situated moment-by-moment organization of interaction, of the intelligibility it has for the participants, and of the membership categories that are achieved and made relevant within the interaction itself. Within this framework, the sense of the plurilingual resources used by speakers can neither be mechanistically related to a set of predetermined factors, such as identities or social structures, nor associated with imputed intentions, strategies or goals of the participants. Instead, the questions asked (and answered through analyses of empirical data) are: how do participants orient to bilingual resources? Which problems are solved by participants’ procedures of exploiting bilingual resources? What intelligibility is given to these resources through the specifi c and local ways in which they are mobilized? What kind of ‘procedural consequentiality’ does the orientation have for the construction of identities, social categories or language diversity, i.e. what are the demonstrable consequences of this orientation and its manifestation in the specifi c sequential unfolding and organization of the interaction? These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.033 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".