The Construction of Teacher Identity in Education for Sustainable Development: The Case of Chinese ESP Teachers
Bibliographic record
Abstract
As the education for sustainable development (ESD) has been advocated in diverse educational contexts, increasingly more attention has been paid to facilitate teachers as the promoters of such educational practice in higher education. Yet, less sufficient research has focused on the ESP (English for Specific Purposes) teachers, who are regarded as the pioneers of ESD practice, and their professional development. This study aims to promote teacher professional development in the area of ESD practice by investigating the identity construction of eight ESP teachers from a northern Chinese university. Drawing on a model of ESP teacher identity, the authors conducted life-history interviews concerning the five frames of identity construction. The results reveal a complex picture of ESP teachers’ professional identity construction. It is revealed that a majority of participants claimed a sense of achievement in their teaching of ESP skills and sustainability competences in graduates for the local socioeconomic development. However, their professional identification is undermined by such factors as the marginalization of ESP as a legitimate discipline, lack of acknowledgement of ESD in curriculum design, and teachers’ professional insecurity resulted from their professional incompetence and low academic status. This study provides a new and innovative perspective on the issue of ESD development in higher education.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".