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Record W4416775193 · doi:10.25071/2564-2855.51

To pronounce or not to pronounce

2025· article· W4416775193 on OpenAlexvenueaboutno aff
Munise Gültekin

Bibliographic record

VenueWorking papers in Applied Linguistics and Linguistics at York · 2025
Typearticle
Language
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsStress (linguistics)PronunciationVariety (cybernetics)MulticulturalismPerspective (graphical)CurriculumSociocultural evolutionPower (physics)

Abstract

fetched live from OpenAlex

As a subtle yet pervasive part of English as a Second Language (ESL) programs, pronunciation instruction for adult newcomers poses linguistic concerns based on historical power dynamics shaped by sociopolitical factors in Canada. Pronunciation courses inadvertently tend to prioritize segmental features and encourage accent reduction for better employability and integration goals and thus disregard the sociocultural identities of learners. Through this critical analysis, I bring a new perspective to “Standard Canadian English” by drawing on language pedagogy, learner and teacher perspectives, and language policies. I examine how Anglonormativity manifests itself in Canadian language classrooms by systemically marginalizing linguistic variety and perpetuating assimilation. The disparity between multiculturalism ideals and classroom realities urges the educational system to seek intelligibility-focused instruction, teacher professional development opportunities, and inclusive curricula that validate diverse linguistic repertoires. Therefore, this paper makes a strong case for systemic improvements to support immigrants in navigating Canadian life by challenging monoglossic norms embedded in language education.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.067
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.067
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.310
Teacher spread0.282 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2025
Admission routes2
Has abstractyes

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Same venueWorking papers in Applied Linguistics and Linguistics at YorkSame topicLinguistic Variation and MorphologyFrench-language works237,207