The Economic Network Resilience of the Guanzhong Plain City Cluster, China: A network analysis from the evolutionary perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it