Coevolution of Economic and Industrial Linkages within the Land-Sea Industrial Structure of China
Bibliographic record
Abstract
The joint development of continental and marine economies has become an important driving force for the upgrading of industrial structures. However, because of the differences in resource endowment and development potential, developing industrial structures and the quality of economic development are uneven among regions. In this study, the added values of three land-sea industries in the three marine economic circles of northern, eastern, and southern China, were employed to clarify the evolutionary behavior of the industrial structure of these three circles on the land and sea; the synchronization, lag, equilibrium, and dislocation of developing the industrial structure were also explored which a gray relational model based on convex judgment and gray time difference analyses were used to construct a relational model from the static and dynamic aspects of the system, and the internal and external linkages of the industrial structure of the three circles were analyzed from the perspective of industrial correlation. The results show that: (1) Correlations among the linkages of the three economic circles in the marine industrial structure, both including and without temporal and spatial differences, and the marine feedback driver, differ markedly. (2) The effects of feedback for marine industrial development from the Eastern Marine Economic Circle were stronger, whereas those of the Southern Marine Economic Circle were weak and those of the Northern Marine Economic Circle were ambiguous. (3) A significant difference was observed in the degree of coevolution among the land-sea industrial structures of these areas. The Northern Marine Economic Circle exhibited a slightly higher degree of coevolution than the other two economic circles, showing a stable trend of coevolution and wide spatial development. The eastern and southern circles displayed high degrees of coordination in developing their industrial structures. The research results provide a reference for regional adjustment and optimization of industrial structure.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".