Bibliographic record
Abstract
French stylization can frequently be observed in the English-speaking media under the form of faked French accents or performed personas as is the case with Poirot’s TV character. In the popular ITV drama adaptation of the well-known eponymous novels of Agatha Christie, dialogues in English are peppered with French words. This paper will analyse the functions of these tokens of bilingualism and will show that they are used as a form of ethnosymbolism (Haarmann 1986). In this article, I argue that the actor’s performance is a representation of the foreign language and culture for a mainly monolingual audience. For Androutsopoulos (2007: 222), it is a matter of ‘styling ethnic otherness for majority audiences’. I relate the staging of salient linguistic traits to folk linguistics and more particularly to the beliefs a speech community carries on another speech community and its ways of speaking. In order to identify popular conceptions regarding transfers from L1 French, the lines of the Belgian sleuth are analysed on lexical, pragmatic and syntactic levels.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".