The Meaning and Practice of Professionalism of EFL Teachers in the Saudi Context
Bibliographic record
Abstract
During the Preparatory Year Program (PYP) year, the English Language Institute (ELI) concentrates on raising the level of English of new students so that they are able to adequately cope with studying in their desired department in the following years. Such a drastic change in the institution’s goals has led to some changes in the professional development programme in order to handle these new changes. For the first part of this research study, a review of the past and current research studies on professional development and professional identity issues will be presented. The second part of this paper will report on a small-based inquiry conducted with four (two male and two female) ELI teachers aiming to explore two important and related issues. First, there is the issue concerning the role of English as a Foreign Language (EFL) teachers in planning and structuring the professional development programmes offered by the ELI, while the second looks to examine the professional identity of the teachers as sensed by the teachers themselves. In the third and final section, a reflection on how this inquiry can lead to the awareness of the teachers’ role (male and female) in training and professional development programmes as well as professional identity perceived by the teachers, which ultimately, should lead to a much deeper interpretation of the status of the English language teacher profession.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".