Synergizing the research, policy, and practice nexus: The roles of EFL teaching research supervisors in China
Bibliographic record
Abstract
A central aim of education is to align research, policy and practice to facilitate student learning. This study examines English as a foreign language (EFL) teaching research supervisors who mobilize knowledge by conducting research, supervising teachers, and supporting policy implementation in Shanghai, one of China’s most successful education systems. It investigates how teaching research supervisors at different education administration levels (i.e. municipal and district levels) fulfil their roles to synergize the nexus between research, policy and practice. A two-cycle qualitative data-coding and analysis approach was employed to interpret data consisting of semi-structured interviews and documents. The findings demonstrate that EFL teaching research supervisors adopted seven lines of work to carry out their roles to conduct research, supervise and support teacher professional development, and support policy implementation. These practices reflect their function as knowledge brokers who mobilize research, policy, and practice in context-responsive ways. The study contributes to theoretical understandings of knowledge mobilization and offers practical implications for strengthening research–policy–practice alignment in education systems.
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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 | 0.021 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".