Book Review: Reynolds et al. (Eds.). (2024). Decentering Advocacy in English Language Teaching: Global Perspectives and Local Practices. University of Michigan Press.
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.046 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".