The Spatial Pattern Evolution of the County Administrative Region in China Based on the Perspective of Geograph
Bibliographic record
Abstract
Taking the county level administrative region of China as the research object, we got some conclusions by analyzing its spatial pattern:(1) the center of gravity of China's county administrative region mainly moved to the west during the feudal society and after the feudal society, began to move to the east. Studies on points area showed that the center of gravity of China's county administrative region in the west mainly moved to the northwest and in the north moved to the northeast. However, the southern region mainly moved to the southwest during the feudal society and after the feudal society, began to move to the east.(2) The whole county level administrative region and partition exerted decentralization based on the analyses of the standard deviational ellipses. From the perspective of the main direction of the county administrative region distribution, the whole country and the north mainly presents the northeast- southwest and the west and the south presents the northwest- southeast and south- north pattern respectively.(3) Based on the point of county level administrative region spatial density pattern, its space continuity and self- organizing continuously strengthen and structural variation caused by spatial correlation was significant. From the point of direction, the degree of homogenization on the Omni- direction decline overall, but after new China rose slightly, the degree of the homogenization of the county density in every dynasty is relatively well and the spatial difference is minimum in the northwest and southeast, but the spatial difference is most obvious in east and west.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".