TÜRK K12 ÖZEL OKULLARINDA YABANCI UYRUKLU ÖĞRETMENLERIN IŞ TATMINI: FENOMENOLOJIK ARAŞTIRMA
Bibliographic record
Abstract
With the increase in global mobility in the teaching profession and the rising number of expatriate EFL teachers, it is important to study this group’s experiences in the host country. This thesis study aimed to investigate the job satisfaction of expatriate EFL teachers in the Turkish private K-12 system. The study was carried out with 4 participants coming from the United States, Canada, and Albania and living and working in Ankara during the 2021-2022 academic year. To reach the objectives, outlined in this study, the researcher collected the data from the expatriate EFL teachers through a survey and a series of three semi-structured interviews. The participants were also asked to journal over the course of one week and write down any specific incidents that affect their job satisfaction one way or another. The data were analyzed separately for each participant after each case was reported, the researcher looked for ways in which the participants converge or diverge, and three themes emerged. Using interpretative phenomenological analysis, the study revealed that the teachers have varying definitions of job satisfaction that converged in their prioritizing relationships, community, achievement, and recognition of achievement; they experienced low levels of job satisfaction, even though at times there were upswings in these levels; and love for the job and community overshadowed by contextual aspects. Following these themes, it became clear that steps should be taken by Turkish private K-12 schools to ensure higher levels of satisfaction for expatriate 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".