Factors influencing the desire for rural residential land arrangement——A case of Ganzhou District
Bibliographic record
Abstract
Under the population pressure,the reserves of land is deficient and degenerated seriously.There is a widespread phenomenon that rural residential land has many waste lands such as disengaged land and obsoleting land.The problem of rural residential land has caused the widespread concern by government and academic circles.Taking Ganzhou District as the research area,we collected data through 784 questionnaires from farmers,researched the personal characteristics and household characteristics variables,the feature of now living house variables,policy feature variables and other characteristics influence to rural residential land arrangement by employing the methods of binary logistic regression.The results of the study show that in the study area 73% of the farmers are willing to do residential land arrangement,and age,family income and currently house structure of farmers influence the desire of rural residential land arrangement obviously.The farmer whose age is older,family income is higher,currently house structure is not satisfied has stronger desire of rural residential land arrangement.The farmer whose age is from 35 to 50 years old,family income is more than 15000 yuan,currently house structure is doby or flaky stone has the strongest desire of rural residential land arrangement.The research of influencing factor of desire of rural residential land arrangement will help realize the causes of desire of rural residential land arrangement,and can give farmers a guide that properly proceed rural residential land arrangement,in addition also can provide reference for that policy of rural residential land arrangement continue to implement for the next step.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".