Reflections on the pedagogical imports of western practices for professionalizing ESL/EFL writing and writing-teacher education
Bibliographic record
Abstract
The teaching of writing in English as a second/foreign language (ESL/EFL) has been a challenging task for many teachers due to its multifaceted nature. This paper is a reflection on ESL/EFL writing teaching in three countries, namely China, Singapore, and New Zealand, with particular reference to professionalizing ESL/EFL writing and ESL/EFL writing-teacher education. It first addresses issues facing EFL writing and writing-teacher education that relate to the offering of English at various levels in China. It then moves on to elaborate on how western pedagogical practices have been implemented in Singapore, especially that of a genre-based pedagogy. Nestled in the context of globalization, I focus on New Zealand, positing that globalization has exacerbated the challenge in teaching ESL writing because of large numbers of students who are seeking higher education in western countries in English as the medium of instruction, and yet their first language is not English. I conclude the paper with recommendations that professionalizing L2 writing (even in school settings) is a mission for all those who are in this enterprise. Proper teacher preparation programs for training L2 writing teachers should be in place in order for this to happen. China needs to critically appraise, and learn from, successful experiences such as Singapore and many institutions in the USA and Canada. New Zealand is yet to formalize ESOL writing teacher preparation programs, where English-as-an-L2 writing-teacher education for primary and secondary schools is still not a priority in most teacher-education institutions.
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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 | 0.026 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.029 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".