Spatial Characteristics and Their Causes of the Urban and Rural Public Service Facilities in Guangzhou
Bibliographic record
Abstract
Making a case study on educational, medical, and recreation and sports facilities, this paper explores the spatial characteristics and their causes of urban and rural public service facilities in Guangzhou with the methods of Kernel density analysis and Path analysis. The results indicate that: 1) Spatial pattern of basic public service facilities in Guangzhou follow the laws of core-edge concentric circles structure, the order of facilities density is: core urban areasnewly-developed urban areasurban-rural fringe areasrural areas, the emergence of deputy center and exurb makes the pattern change towards a multi-polar direction; 2) Spatial patterns of different types of facilities are basically the same, but have different features, spatial intensity of medical facilities is the highest among the three kinds of facilities, that of educational facilities the next, and that of recreation and sports facilities the lowest; 3) Inter-regional spatial distribution is uneven, showing obvious administrative division mark, spatial intensity of the facilities in Yuexiu, Haizhu and Liwan District is the highest, much differs from that of Zengcheng and Conghua, administrative boundaries become separate lines to prevent Kernel density isolines from unobstructed outward expansion. 4) Results of Path analysis show that the population factor is the most important factor for the equalization of basic public services, other factors in the order of importance are as follows: Infrastructure investment(x10)Agriculture as a share of GDP(x2)industrial output(x3)revenue(x4) GDP(x1)level of urbanization(x9)expenditure(x5)Development history(x11)Total retail sales of consumer goods(x7)use of foreign direct investment(x8) total fixed asset investment(x6).
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".