Network structure of 'space of flows' in Jilin Province based on telecommunication flows
Bibliographic record
Abstract
Information communication is an important expression of interaction between two cities, and it is also a key element to build the city network. This study proposes to map the network structure of 'space of flows' based on the actual observed telecommunication flows,with Jilin Province as the case area. Specifically, the call durations via fixed- line telephone are employed to measure the information flows occurred between cities. The cities at the county level or above are treated as research units. To be reliable, a synthetic method composed of principal component analysis, C-Value and D-Value hierarchy analysis, dominant flow analysis, the minimum spanning tree method is utilized to map out the structure. The research reveals the following aspects.(1) The 'space of flows' in Jilin Province is a hierarchical network, which centers on Changchun. In this network, Changchun, Yanji,Tonghua and Gongzhuling are the 1st-level leading cities; Jilin, Baicheng, Baishan, Liaoyuan,Songyuan and Siping are 2nd-level leading cities, and the other cities in Jilin are subordinate cities.(2) Administrative division plays a fundamental role in the formation of the current pattern.(3) Changchun is a unique center, but on the contrary to our previous understanding,Jilin is not that 'centric', and the interaction between Changchun and Jilin is not that strong either.(4) Surprisingly, the two cities of Gongzhuling and Dunhua at the county level, play important roles in the network of 'space of flows'. Gongzhuling tends to be blended in the Changchun metropolitan area, and Dunhua becomes a key node in the eastern Jilin. The regional connectivity functions of the two cities need to be improved.(5) Siping and Lishu,have strong interaction with each other, and are supporting a further integration strategy of the two neighboring cities.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".