Informal Land Development on the Urban Fringe
Bibliographic record
Abstract
Urban fringes are an important part of urban growth. In addition to formal land markets, a variety of informal land development methods make urban fringes the most dynamic and complicated areas. The analysis of land transfer and development systems in these areas opens a significant window to understanding the modern processes of urbanization and human and property rights in urban areas in China. This study uses Shanghai as a case study target and identifies specific modes of local land development and investigates how collective participants, government agencies, regulatory policies, and various actors are involved in land development and decision making. The in-depth analysis and case studies indicate that the variety of informal land markets in Shanghai reflects the inherent demands of the market for allocation of land resources within the constraints of the given system and against the given development background. However, conflicts between the mode of the market and the existing institutional constraints reflect the uncoordinated development of the land and the economic and social development around the urban fringe. The empirical results of this paper suggest that government administration should improve the land market system, strengthen the planning of control and guidance, rationalize the distribution of interests in land development, and strengthen the supervision of management of land development enterprises. Instead of fragmented aspects, this paper proposes a systematic analytical approach to understanding the informal land development in a city from an urban planning and land resource management perspective.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".