Knowledge mobilisation and education policy making in China
Bibliographic record
Abstract
Acknowledgement This chapter could not have been finished without support from the faculty of education, Beijing Normal University (BNU). The chapter is a team effort. We would like to give our thanks to the Department of Development at BNU for the provision of data. We are grateful to all the people who have provided support. We are grateful to Professor Camel from Queensland University of Technology, Australia, for designing the topic of knowledge mobilisation and to Professor Julia Pan from OISE, University of Toronto for her hard work in editing the chapter. Special thanks are given to two professors from Beijing Normal University, Professor Zuoyu Zhou and Professor Jiayong Li. Introduction This chapter uses the lens of knowledge mobilisation (KM) to look at issues in education in the People's Republic of China. The authors try to answer the following questions. What is the current state of KM work in China? What are the characteristics of education policy making in China and how does KM relate to it? The chapter is divided into five sections. The first part provides some background about the Chinese education system, research capacity and the major achievements of recent education reforms. The second part examines education research institutes to see how they have been influencing policy, especially at the national governmental level. In the third part, the features of KM in China are portrayed. The fourth section describes four challenges China is facing. In the last part, a general picture of KM is outlined. The Chinese education system and education research An important background point to keep in mind about China is size and scale. China has a population about four times as large as the next largest country in this collection – the US. It is also geographically large and ethnically diverse. All of this creates huge challenges in delivering high-quality higher education and research in a country that 60 years ago was desperately poor. It also means that the numbers involved in anything national are massive. Compare Canada's few thousand education researchers and faculty members with the 230,000 or so in China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".