The Economic Network Resilience of the Guanzhong Plain City Cluster, China: A network analysis from the evolutionary perspective
Bibliographic record
Abstract
Abstract This article applies the network analysis to evaluate the resilience of economic network in Guanzhong Plain City Cluster (GPCC) and examine the impact of network structural properties on economic resilience, thus providing an innovative research perspective and a theoretical framework for evaluating the economic resilience of the city cluster. A modified gravity model is introduced to construct the economic network. Three structural properties of the network, hierarchy, assortativity, and cohesion, are used to evaluate the resilience of the GPCC from 2008 to 2018 and illustrate the characteristics of the resilience. The results show that the economic network of the GPCC is strongly hierarchical with a growing trend, a declining disassortativity, and a weak cohesion. Although the network has formed a core‐peripheral structure, its hierarchy and disassortativity would result in low resilience and high vulnerability, at the risk of external shocks to the GPCC. The impact of the network structure on economic resilience is analyzed by using a regression model, which verifies the validity of applying the network theory to resilience analysis. The results suggest that improving the interactions and economic connections between core and peripheral cities will strengthen the resilience of the GPCC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".