The Temporal and Spatial Evolution of Marathons in China from 2010 to 2018
Bibliographic record
Abstract
The purpose of this study is to explore the spatial distribution pattern and influencing factors of the Chinese marathon. Geographic Information System (GIS) related spatial analysis tools were used to calculate the following—averaged nearest neighbor index, nuclear density analysis and hot spot analysis among others. The spatial distribution evolution characteristics and the influencing factors of eighteen Chinese marathon events in 2010, 129 in 2015 and 342 in 2018 were analyzed. The results show that (a) in 2010 the nearest neighbor ratio was 1.164714, Moran’s I was −0.010165 (type: Random), in 2015 it was 0.502146, Moran’s I was 0.066267 (type: Clustered) and in 2018 it was 0.531149 and Moran’s I was 0.083485 (type: Clustered); (b) in 2010 there was a 333.6 km search radius; the core circle of the Yangtze River Delta was adopted. In 2015 and 2018, a search radius of 556 km was adopted, which was respectively obtained from the core circle of the Yangtze River Delta, the core circle of Beijing-Tianjin-Hebei and the core circle of East China; (c) according to the Z-value data, East China and North China in 2015 passed 95% confidence in five provinces and municipal hot spots, passed 90% confidence in three hot spots and passed 95% confidence in Chongqing Cold Point. In 2018, East China, North China, Central Region and eight other provinces and cities’ hot spots passed 95% confidence, four hot spots passed 90% confidence, the Tibet Autonomous Region cold spot passed 90% confidence. Conclusion: The overall distribution of marathon events is greater in the eastern region than the western region, greater in the southern region than the northern region and greater in coastal regions than the inland regions; the nuclear density distribution has spread from the Yangtze River Delta mononuclear circle in 2010 to the core circle of the entire East China region. Moreover, it spread to North China, Central China and South China; the distribution of hot spots spread from Shanghai, Jiangsu and Zhejiang to the entire North China and East China regions. During the past thirty-eight years of development of the Chinese marathon, it has been divided into three stages due to different political, economic and social environments.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".