FAILURE TO ACHIEVE DEVELOPMENT IN SPITE OF A SERIES OF REFORMS: WHAT IS WRONG WITH EFL TEACHERS’ ENGLISH PROFICIENCY?
Bibliographic record
Abstract
This study focuses on one of Ethiopia’s unfading education policy problems, namely the poor English proficiency of EFL teachers and their students. Qualitative data was collected through unstructured questionnaire and participant observation from twenty-five randomly selected highly experienced EFL schoolteachers and tertiary EFL educators coming from all corners of the country. Relevant archival data were also collected. Besides, four expert informants were also involved as critical consultants for the study. The data was analyzed qualitatively with the reflective and iterative constant comparative method. The results show that for Ethiopian EFL teachers, the problem of the so-called “poor” English proficiency is actually an outcome or a result of poor socio-educational preconditions that inhibited their holistic development as dignified, full-fledged professional citizens. Particularly, meager living conditions, alienating working environments, and a totalitarian policy and practices known as “Cascade Model” are the chief stumbling blocks to their development as fully proficient EFL teachers.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".