MétaCan
Menu
Back to cohort
Record W4413063051 · doi:10.1111/ijal.12818

Identity, Politics and Power: Stories of Teaching Language in Quebec

2025· article· en· W4413063051 on OpenAlexafffundabout
Katherine Hardin, Philippa Parks, Caroline Riches

Bibliographic record

VenueInternational Journal of Applied Linguistics · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversité de SherbrookeMcGill University
FundersSocial Sciences and Humanities Research Council of CanadaMcGill University
KeywordsIdentity (music)NegotiationSociologyPoliticsPedagogyPower (physics)Isolation (microbiology)LinguisticsPsychologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

ABSTRACT Language teaching is an inherently political act, and in Québec it is inextricably tied to questions of cultural belonging and national identity. This study explores the experiences of English as a second language (ESL) teachers in French‐medium schools in Québec. Drawing on critical applied linguistics and identity theory, it examines how pre‐service and in‐service ESL teachers navigate the tensions between the teaching of ESL and Québec's language policies. The study restories qualitative interview and survey data to explore three key themes. The resulting stories show how ESL teachers negotiate their professional and linguistic identities as they move through their school environments, demonstrating that in doing so, they often face social isolation and misperceptions about their linguistic identity and affiliation. These findings point to the need for teacher education programs to better prepare pre‐service ESL teachers for these realities by equipping them with strategies that will support their resilience and self‐advocacy.

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.509

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0400.019
Scholarly communication0.0080.004
Open science0.0030.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0070.001

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 head0.019
GPT teacher head0.453
Teacher spread0.433 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

Explore more

Same venueInternational Journal of Applied LinguisticsSame topicMultilingual Education and PolicyFrench-language works237,207