Understanding teacher leadership identity: the perspectives of Chinese high school teachers
Bibliographic record
Abstract
The literature has shown that teacher leaders’ self-identity is a crucial aspect of teacher leadership development. However, less is known about the factors that influence the development of teacher leadership identity in the Chinese education context. By conducting semi-structured interviews with 23 secondary school teachers and administrators, this study aimed to answer two questions: What are the factors influencing teacher leadership identity development, and how do these factors influence teacher leadership identity development in the Chinese education context? Research identifies diverse factors that can influence teacher leadership identity. The personal factors comprised a sense of responsibility, the teachers’ personality, personal working experience, beliefs about leadership capacity. The organizational-level factors included the relationship among organization members, the role of principals, the formal selection mechanism for teacher leaders, personal working capacity, time, and energy, opportunities for the emergence of leadership, the nature of teachers’ work, and the atmosphere of the school. At the societal level, national cultural values also influenced teacher leadership identity. This article provides practical experience of developing teacher leadership identity in the Chinese context and tries to supply the cross-culture understanding of education administration from the perspective of improving teacher leadership identity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".