Bibliographic record
Abstract
Abstract Discourse Markers (DMs) are particularly susceptible to borrowing between languages and several approaches can provide a framework to analyse speech in multilingual contexts. This paper examines a structural and a pragmatic-functional perspective: Myers-Scotton’s Matrix Language Frame (MLF) model and Matras’ Pragmatic-Functional (PF) perspective. It considers how DMs fit into these approaches and how they deal with code-switching in Kreol Morisien-French multilingual conversations. As it is rare to consider the same linguistic data from these different linguistic perspectives, this paper explores whether they are competing models or may offer complementary perspectives. MLF sees languages as distinct entities which are switched between, while PF involves context-appropriate selection of components from a complex repertoire. Matras’ pragmatic-dominance hypothesis is also explored through correlations with language use. Although the approaches emphasise different aspects of multilingual speech, it is concluded that together they can offer complementary perspectives on Mauritian discourse, despite being conceptually difficult to reconcile.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".