Introducing Participatory Curriculum Development in China's Higher Education: The Case of Community-Based Natural Resource Management
Bibliographic record
Abstract
This article describes and reflects on a novel course developed at China Agricultural University to introduce Community-Based Natural Resource Management at the postgraduate level. This course, part of a larger educational renewal initiative addressing the current reform of China's higher education system, was developed through a participatory curriculum development methodology bringing together teachers and researchers from five different organizations, as well as a dynamic group of MSc and PhD students. The course development process and the actual delivery in the classroom and in the field were guided by insights from adult teaching and learning theory. These were adapted to the Chinese reality. Results assessed to date from the experimentation with this completely new approach in China encompass conceptual, attitudinal, methodological, and practical changes. The experiences and insights accumulated so far serve as entry points for the expansion of participatory curriculum development practice in China. They also provide a ground for deepening learning theory.
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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 | 0.022 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".