The Impacts of In Situ Urbanization on Housing, Mobility and Employment of Local Residents in China
Bibliographic record
Abstract
Rapid economic growth and infrastructure development force in situ urbanization in locations where people from rural areas gain urban residency without experiencing long-distance geographical relocation. However, the impacts of in situ urbanization on farmers’ and other residents’ well-being remains unclear, and there are some arguments about the idea that “urbanization of people lags behind urbanization of land” in China. Therefore, this study firstly finds a reasonable way to measure in situ urbanization: the transfer of rural-urban division codes. On this basis, by applying the PSM-DID method, we use national census data to explore the impacts of in situ urbanization on farmers from the perspective of housing, mobility and employment. The research results show that after the in situ urbanization, the possibility of farmers moving into non-self-built high-rising buildings increases, while the possibility of farmers leaving the county for employment decreases. Besides, the employment structure in the county where in situ urbanization takes place has shifted from primary industry to secondary and tertiary industry. Moreover, this paper also discusses the spillover effects of in situ urbanization on other residents in the county. Our study shows that in situ urbanization can improve residents’ well-being and offers sustainable land-people integrated urbanization.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".