Relationship between Population Urbanization and Basic Public Service in the Context of the New Urbanization—Take Jilin Province for Example
Bibliographic record
Abstract
Taking the Jilin Province as a study case, this article used the coupling and coordinating model and decoupling state model to analysis the relationship between population urbanization and basic public service since 2003. Conclusions are drawn as follows: 1 The development of population urbanization mainly manifested as the temporal unbalance and spatial stability, the high values are centralized in the border area of eastern and central core cities, while the low value clusters in the western inland area and resources-exhausted cities; 2The development of basic public service present the increasing trend with the exception of Jilin and Siping, the regional differentiation characteristic of basic education service and social security service is obvious, the regional differentiation of medical healthy service and municipal facility service is not obvious, and the polarization phenomenon of cultural sport service is prominent; 3The couplings between population urbanization and basic public services are at the resisting state, and the coordinating degree is relatively poor, the coupling and coordinating degree of population urbanization and culture sport service shows a downward trend; 4 The decoupling degrees between population urbanization and basic public services gives priority to strong decoupling, weak decoupling, strong negative decoupling and weak negative decoupling.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".