Hierarchies of Authenticity in Study Abroad: French From Canada Versus French From France?
Bibliographic record
Abstract
Abstract For many decades, Francophone regions in Canada have provided language study exchanges for French as a second language (FSL) learners within their own country. At the same time, FSL students and teachers in Canada continue to orient to a native speaker standard associated with European French. This Eurocentric orientation manifested itself in a recent study examining conceptions of authentic language among Canadian FSL teachers on professional study abroad in France. Taking an interactional perspective (De Fina & Georgakopoulou, 2012), this article examines how the teachers negotiated discourses of language subordination (Lippi-Green, 1997) that construct Canadian French as less authentic than French from France. Findings show some teachers drawing on this hierarchization of French to “authenticate” (Coupland, 2010) an identity as French language expert, either by contrasting European and Canadian varieties of French or by projecting France as the locus of French language and culture as exclusively representative of authentic “Frenchness.” Résumé Depuis des décennies, les régions francophones au Canada ont offert aux apprenants de français langue seconde (FLS) des programmes d’échange linguistique dans leur propre pays. Toutefois, les étudiants et les enseignants de FLS au Canada ont tendance à toujours se référer à la norme standard du locuteur natif parlant le français européen. Cette orientation eurocentrique a été relevée récemment dans une enquête examinant la notion d’authenticité linguistique auprès d’un groupe d’enseignants de FLS à la suite d’un stage de formation en France. S’appuyant sur une perspective interactionnelle (De Fina et Georgakopoulou, 2012), cet article examine la façon dont les enseignants font face aux discours de subordination linguistique (Lippi-Green, 1997) qui contribuent à renforcer l’idée que le Canadien français est moins authentique que le français de France. L’analyse montre que certains enseignants utilisaient cette hiérarchie du français pour se justifier comme experts linguistiques en français dans leur processus d’authentification (Coupland, 2010) en contrastant les variétés canadiennes et européennes du français ou en privilégiant le français et la culture de la France comme seule variété authentique.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.020 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".