World Englishes, Critical and Feminist Pedagogies Coalition in Pre-service Teacher Training
Bibliographic record
Abstract
In an attempt to overcome the fragmentation of theoretical and practical post-structuralist strands applied to the teaching of EFL, this article explores how critical and feminist perspectives can inform the TESOL practice through the inclusion of the voices and experiences of those who have been excluded from dominant discourses according to the World Englishes classification. After exposure to basic readings on feminist and critical pedagogies, a group of pre-service teachers enrolled in the Master's degree in teaching at the University of Burgos in Spain, were asked to design teaching activities for the selected authentic materials. Malala Yousafzai's speech to the UN in 2013 was selected so that the pre-service teachers would face nonnative texts. Through the didactic exploitation of her discourse, gender, power, identity and violence issues were addressed. Results indicate that coalition of World Englishes, feminist, and critical pedagogies enhance resistance to dominant discourses, foster awareness in teaching practices and contribute to social transformation. Further scholarly reflection and classroom practices are suggested on the relationship between the use of English as a globalized language for training purposes and its relationship with the particular struggles of the oppressed.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".