Research on Layout Optimization of Villages in Poverty-Stricken Counties —A Case Study of Wangmo County, Guizhou, China
Bibliographic record
Abstract
Against the background of rural revitalization in China, this study takes Wangmo County as the study area, and uses mathematical models such as entropy method and rank-size rule to quantitatively evaluate the rural development potential of Wangmo County and rural development scale and hierarchical structure in Wangmo County. Based on this, the study puts forward suggestions for village layout optimization. According to the results, (i) evaluation elements of rural subject, industrial development, resource endowment, and habitat environment in Wangmo County are presented in the spatial pattern of being scattered as a whole and be agglomerated locally. (ii) The rank-size distribution of village is that there are more villages in the medium-ranking position, while there are fewer high-and-low-ranking villages with spindle structure of “being small on both ends, and large in the middle”, showing that the agglomeration of village elements has a weak degree of spatial polarization. (iii) By comprehensively evaluating results and field investigation situation, a township hierarchy of central village-general village-merged village is constructed to divide the development types of three rural areas, including agglomeration type for improvement, equilibrium, and stable type, as well as relocation and merger type. The results of the study in the case area can provide a reference for the local scientific response to the decreasing trend of rural population and change the predicament of low efficiency of public resource allocation caused by small-scale and scattered distribution of the rural areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".