MétaCan
Menu
Back to cohort

Book Review: Reynolds et al. (Eds.). (2024). Decentering Advocacy in English Language Teaching: Global Perspectives and Local Practices. University of Michigan Press.

2025· article· en· W4412773904 on OpenAlexaboutno aff
Xiaoxiao Kong

Bibliographic record

VenueTESOL in Context · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsPublishingSociologyMedia studiesPolitical scienceLibrary scienceManagementLawComputer scienceEconomics

Abstract

fetched live from OpenAlex

In recent decades, along with the emerging inquiry into the social and political dimensions of language education (e.g., Crookes, 2022), advocacy in English language teaching (ELT) – efforts on behalf of English language learners to promote social justice language education (Linville & Whiting, 2019) – has received growing attention. While there is growing global interest in ELT advocacy, the majority of published studies are situated within the Global North (e.g., United States, Canada, United Kingdom, Australia); advocacy research in postcolonial and/or underrepresented regions remains limited (Guerrero Nieto, 2020). Decentering advocacy in English language teaching: Global perspectives and local practices contributes to this body of work through the narratives of advocacy efforts within 11 diverse geopolitical and educational contexts in Africa (Nigeria, Cameroon), Central America (Belize, El Salvador), Asia (Vietnam, Laos), Middle East (Türkiye, Israeli and Palestinian Territories), and South America (Paraguay, Uruguay), each documented and reflected upon by the advocates themselves. It serves as a valuable resource for educational professionals working within the space of ELT advocacy, or students and researchers learning about current ELT advocacy efforts in the global context.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.722
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.277
Teacher spread0.264 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueTESOL in ContextSame topicSecond Language Learning and TeachingFrench-language works237,207