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Record W3080471038 · doi:10.1080/15427587.2020.1805613

Exploring the language ideology of nativeness in narrative accounts of English second language users in Montreal

2020· article· en· W3080471038 on OpenAlexaboutno aff
Giuliana Ferri, Viktoria Magne

Bibliographic record

VenueCritical Inquiry in Language Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsIdeologyNarrativeSociologySuperordinate goalsContext (archaeology)MulticulturalismMultilingualismFirst languageSociolinguisticsLanguage ideologyPsychologyPoliticsPedagogyHistoryPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

The current study qualitatively examines 23 interviews with English second language users focusing on their lived experiences of communicating in the context of multicultural and multilingual interactions in Montreal. The interpretative phenomenological analysis of data reveals two superordinate themes: the idealized native speaker of English and ambivalent attitudes toward linguistic diversity which uncover the contested and shifting nature of language ideologies. The themes offer a narrative of the ideology of nativeness, intersecting with current studies in multilingual practices in globalized contexts. The authors suggest that the model of idealized native speech creates unrealistic expectations in English second language users regarding their own linguistic performance and their self-image as users of English. The study proposes the adoption of Lx speaker in order to challenge the monolingual bias inherent in the native and non-native speaker dichotomy.

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)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.598

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0150.018
Scholarly communication0.0070.003
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.261
GPT teacher head0.509
Teacher spread0.248 · 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 designQualitative
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

Citations26
Published2020
Admission routes1
Has abstractyes

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