The Changing Dynamics of Land-Use Change in Rural China: A Case Study of Yuhang, Zhejiang Province
Bibliographic record
Abstract
There is considerable debate in the literature on the extent and magnitude of Chinese rural land-use change since the onset of reforms in 1978. Moreover, there is little agreement on the factors leading to rural land-use change. Urbanization of the countryside, rural industrialization and housing development, lack of systematic agricultural protection policies and mechanisms for their implementation, and land-development programs are often considered responsible for the conversion of agricultural land to nonagricultural uses. Through a detailed case study of Yuhang in Zhejiang Province, the author attempts to unveil the complexity involved in land-use change in rural China. A variety of data sources, including published and unpublished official documents and statistics, local gazetteers, key informant interviews, and field observations, are employed. The results of the case study indicate a substantial reduction in agricultural land over the last twenty years. However, the path of this decline has not been smooth. The second decade of reform saw much evidence of decline in agricultural land. Spatially, the most severe decrease in agricultural land has been in areas close to urban centers, and with high accessibility. Urban sprawl, rural industrialization, and housing development have all contributed to the loss of agricultural land. But the author concludes that governments at various levels have played contradictory roles in the process of change. Whereas the governments have made a series of policy initiatives to protect agricultural land, they have, however, been the most important players in destabliizing the land base for agricultural production.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".