Individual and collective agencies in China's curriculum reform: A case of physics teachers
Bibliographic record
Abstract
Abstract This study explores how physics teachers in a high school negotiate the relationships between individual and collective agencies in the context of the on‐going curriculum reform in China. Drawing on Bandura's social cognitive theory, the study employs ethnographical methods including observation, interviewing, and the researcher's and teachers’ reflective journaling through the researcher's involvement with various school activities. The findings indicate that collective teacher agency creates a platform for individual teachers’ professional development, a conducive culture for teacher collaboration, and provides concrete examples that individual teachers can constantly refer to, reflect upon, and learn from for reform implementation. The results offer an understanding of the influences underlying physics teachers’ agency deployment as they engage with curriculum reform processes, especially the negotiation between individual and collective agencies. The findings justify a case for preparing physics teachers on how to deploy both individual and collective agencies in the face of the complicated social structures and ultimately shed light on the desired curriculum decentralization in the Chinese school system.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".