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Record W7020145490

Language and Racial Attitudes toward French\nVarieties in a Second Language Learning\nContext

2022· article· en· W7020145490 on OpenAlexaboutno aff

Bibliographic record

VenueProject Muse (Johns Hopkins University) · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEducational and Organizational Development
Canadian institutionsnot available
Fundersnot available
KeywordsSociolinguisticsSocial representationPerspective (graphical)Speech communication
DOInot available

Abstract

fetched live from OpenAlex

La langue est une force sociale puissante qui va au-delà de la simple communication de contenu. Les apprenants peuvent montrer une préférence pour certaines variétés par rapport à d’autres en raison de stéréotypes linguistiques et de la conscience que les accents peuvent mener à une série de désavantages sociaux et professionnels. La présente étude examine la hiérarchisation de diverses variétés de français au Canada et l’interaction entre variété et race. Un test du locuteur masqué modifié a été utilisé pour recueillir des données attitudinales auprès de 94 participants suivant des cours de français à l’Université de la Colombie-Britannique à Vancouver. Cette étude visait à décortiquer la hiérarchisation perçue de cinq variétés différentes de français (de Moncton, Québec, Vancouver [français langue seconde], Abidjan et Paris) et à trouver une corrélation possible entre l’évaluation des variétés de français et la racialisation des locutrices. Les participants ont évalué les locutrices selon quatre critères : le statut, la solidarité, la compréhensibilité et la perspective générale. Les résultats ont révélé une nette hiérarchie basée sur les attitudes. Pour la plupart des énoncés, les participants ont évalué plus favorablement les locutrices du français québécois et européen. Fait intéressant à noter, ils ont également évalué les locutrices de français langue seconde plus favorablement que les locutrices du français africain et acadien. Cependant, pour chaque variété de français, les voix associées aux locutrices noires ont été évaluées plus favorablement que celles associées aux locutrices blanches. Abstract: Language is a powerful social force that does more than merely communicate content. Language learners can show preference for certain varieties over others due to linguistic stereotyping and the awareness that accents can lead to an array of social and professional disadvantages. This study explores the hierarchization of different varieties of French within Canada and the interplay of variety and race. A modified matched-guise test was used to gather attitudinal data from 94 participants undertaking Frenchlanguage courses at the University of British Columbia in Vancouver. The goals of this study were to unpack the perceived hierarchization of five different varieties of French (from Moncton, Quebec City, Vancouver [a non-native speaker], Abidjan, and Paris) and to identify a possible correlation between the evaluation of varieties of French and the racialization of speakers. Participants evaluated speakers on four dimensions: status, solidarity, understandability, and general perspective. The findings revealed a clear hierarchy based on attitudes. For most statements, the participants evaluated speakers of Quebec and European French more favourably. Interestingly, they also evaluated the non-native speaker of French more positively than the speakers of African and Acadian French. However, for each variety of French, the voices associated with Black speakers were evaluated more positively than those associated with White speakers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.422
Threshold uncertainty score0.839

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0040.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.012
GPT teacher head0.207
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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