A Brief Analysis on the Development Strategies for New-Type Urbanization Simulated by Demographic Factors: Based on Real Evidence in Chongqing
Bibliographic record
Abstract
Population urbanization is an important part of optimized distribution of factors during the process of new-type urbanization. This paper, from the perspective of the current situation of Chongqing’s population, adopts qualitative and quantitative methods to analyze the number, structure, migration, and spatial distribution of population in this city. The study finds that there will be a rapid trend of people moving to the west and to the urban areas. Besides, aging will be a problem for a long period of time, and there might be a shortage of labor resources and immigrant labor force will increase, finally in the long run, the low educational level of local population will affect the transformation of development model in Chongqing. Therefore, during the process of urbanization, on one hand, the strategy of reasonable population flow into western and urban areas should receive enough priority, and on the other hand, household-registration system should be further reformed; demographic structure should be improved; quality of labor force should be enhanced; agricultural population should be transferred as planned; urban system and industrial distribution should be bettered according to demographic characteristics so as to promote synchronized development of agricultural modernization, industrialization, informatization, and 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".