From doctor to facilitator: reflecting on the metaphors of early career EFL teachers
Bibliographic record
Abstract
When language teachers enter a classroom to teach in their early career years, they hold many different beliefs and feelings about how to conduct their classes that for the main part remain at the tacit level of understanding. However, it is important for early career language teachers to become aware of these beliefs and feelings so that they can critically reflect on their significance during this challenging period. Metaphors can offer early career teachers a rich means of identifying their experiences and beliefs that underpin their understanding of teaching and learning a second or foreign language. This qualitative study sought to contribute to the discussion of the experiences of four early career English as a foreign language (EFL) teachers through their use of metaphors to describe their personal understanding of their beliefs and feeling. Specifically, the case study examined the metaphors used by one teacher in her 2nd year, another in his 3rd year an additional teacher in his 4th year, and one in his 5th year of teaching. Results indicate that teachers in their 2nd and 3rd years chose personal metaphors that ‘diagnose’ deficits and thus must be in control, while in their 4th and 5th years the teachers wanted to motivate and facilitate the learning process rather than control it.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".