Localizing CLT: A Case Study of a Western-Trained Chinese EFL Teacher at a Chinese University
Bibliographic record
Abstract
Communicative Language Teaching (CLT) is a second language teaching method prevalent in most English teacher training programs in the Western world. The majority of teacher trainees in CLT-based programs learn English as a foreign language in order to return to their home countries and become English as Foreign Language (EFL) teachers. In many cases, students report that the CLT training environment varies greatly from their experiences as an EFL teacher. This paper investigates the perception of one Western-trained EFL teacher of CLT in China and the strategies she developed to balance communicative methods and traditional Chinese teaching. Realizing the obstacles to implementing CLT in her classroom, Laura developed strategies to adjust to the Chinese context. Using both her learning experience of growing up in China and her educational experience in the United States, Laura was able to navigate conflicting pedagogical backgrounds.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".