The Expansion Mechanism of Rural Residential Land and Implications for Sustainable Regional Development: Evidence from the Baota District in China’s Loess Plateau
Bibliographic record
Abstract
Rural residential land is the main space of a farmer’s life, rural culture, and social relations. Prior research of rural residential land has focused more on its evolvement in plain and traditional agricultural areas. Yet, there is no clear picture of rural residential land expansion, especially in ecologically fragile areas. This study analyzed the characteristics of rural residential land expansion based on 30 m spatial resolution land-use datasets of the Baota District of Yan’an City, Shannxi Province, and further explored the influencing factors and mechanisms of rural residential land expansion through binary logistic regression (BLR) modeling. Our findings indicated that the area of rural residential land in the Baota District increased by 116.16% during 1990–2015. More than 75% of the residential land expansion came from the occupation of cropland. Moreover, rural residential land expansion was heterogeneous in the rural regional system. The expansion scale, speed, and mode diversity of rural residential land decreased with the increased distance to urban built-up areas. Geographical conditions and resource endowments are the primary internal driving factors; urbanization and policy implementation are two major external driving forces. The authors suggest that the realization of regional sustainable development in ecologically fragile areas should strengthen urban–rural integration, focus on constructing central towns, and ensure ecological protection measures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".