From public information to public consultation : improving the land use master planning process at the county and township/town levels in China
Bibliographic record
Abstract
The purpose of this study is to examine public participation policies and the resulting practices in land use planning both in Canada and China and to explore the possibility of applying the Canadian public consultation experience in a Chinese context.In China, along with rapid urbanization and industrialization, the conflicts of interest in land use among stakeholders have become increasingly complicated and multidimensional.The literature review demonstrates that there is a need to enhance the democratic nature of land use planning by improving the level of public participation in China.Several participatory mechanisms, such as public notification and public hearing, have been used in the land use planning process in the last decade in China.However, the level of public participation in land use planning remains very low and the opportunities for the public to be involved are also quite limited.This study interviewed seven Chinese land use planning practitioners from three different groups, representing policy regulators and planning officials from the central government, planning officials from the local government at the county and township/town levels, and planners from land use planning institutes.The research suggests that the present public participation requirements in the Chinese land use planning system can be improved by incorporating the features available in the Canadian land use planning system.However, given the social, economic, political, and cultural differences between these two countries, some techniques often implemented in Canada may be difficult or require a long-term effort to be successfully implemented in China.Based on the results of the analysis, this study provides recommendations for improving public consultation in the land use master planning process at the county and township/town levels through enhancing the effectiveness of land use planning regulations.It also identified three areas for further research, including research respecting the general farmers' attitudes towards the effectiveness of the current public participation practice in China, evaluation of the implementation of the proposed recoÍlmendations, and investigation of how the improvements of the planning mandates may affect the planning practice at the local level.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".