Translating policies into practice: the role of middle‐level administrators in language curriculum implementation
Bibliographic record
Abstract
This study explores middle‐level administrators' perceptions of the implementation of English as a foreign language curriculum policies in the Chinese tertiary context. Drawing on data collected from interviews with the department heads of six universities in a north‐western city in China, the article examines their perspectives on the national language policies and their roles in ensuring the implementation of these policies. The findings revealed a discrepancy between policy‐makers' intentions and administrators' implementation. Policy‐makers designed general, open‐ended and abstract policies to offer local universities some flexibility and autonomy in their delivery. However, administrators as intermediary individuals interpreted the open‐endedness of the curriculum policies in a different way than the policy‐makers had intended. Instead of using the built‐in flexibility to tailor methods of helping students to gain proficiency, they placed their emphasis on only one outcome – students' good scores on the national English test. The findings of this study point to the critical role that the department heads as middle‐level administrators must play in translating policies into practice, as well as underscoring the need for them to provide the necessary motivation and resources for the implementation to occur. These findings are relevant not only in the Chinese context, but also in other educational systems, as language curricula share commonalities in other ESL and EFL countries, which can learn useful lessons from the current study.
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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 | 0.039 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".