From Global Dependence to Local Expertise: An Interview with Rama Mathew
Bibliographic record
Abstract
Rama Mathew is an English Language Teaching (ELT) consultant and retired as Professor of Education from the University of Delhi where she was also Dean of the Faculty of Education. She taught at the English and Foreign Languages University (EFL-U, formerly CIEFL, the Central Institute of English and Foreign Languages) Hyderabad for over 23 years. She was Head of the Research, Monitoring and Evaluation Unit of English in Action project in Bangladesh. She has been involved in several teacher development and assessment projects and published articles and books in the area. She was a lead mentor for ARMS (Action Research Mentoring Scheme) and for ELTRMS (ELT Research Mentoring Scheme), both British Council funded schemes. She has completed multiple projects in India, Bangladesh, and Sierra Leone, where she supported teachers to carry out classroom-based research. Her research interests include language assessment, teaching English to young learners, continuing professional development (CPD) of teachers, multilingual education and making English accessible to learners online. She won the British Council’s South Asia ELTON award for Outstanding Achievement in 2024.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".