Structure of Interregional Economic Interactions as a Factor in the Formation of System Effects in National Economies
Bibliographic record
Abstract
This paper analyzes the structures of interregional economic interactions in Japan, EU countries, China, Canada, Australia, and Russia and attempts to answer the question of what features distinguish the structure of interregional economic interactions, in which the maximum values of spatial multipliers are achieved. The analysis is based on the central places concept that formalizes the main criteria for typologizing the structures of interregional economic interactions. In accordance with this concept, each structure considered in the article is evaluated for compliance with the functional hierarchy and dominance (in ensuring the homogeneity of the national economic space) of the effects of specialization or agglomeration. The calculations show that only in the case of Australia it is inappropriate to speak about the existence of a functional hierarchy; in other countries there clearly are leading regions that play the role of central places in providing the rest of the regions with goods and services. At the same time, the set of countries united by the criterion of the presence of functional hierarchies in them is not homogeneous in terms of the role of different types of effects in the formation of hierarchical structures. The work explains the variation in the values of spatial multipliers depending on what variant of the structure of interregional economic interactions characterizes the national economic space, and within the framework of this explanation the consequences of illusions of a simple solution to the complex problems of spatial organization of the economy of a ?large? country (in a geographic sense) are clearly demonstrated. The conclusion is formulated that the functional hierarchy based on the implementation of specialization effects and their reasonable combination with agglomeration effects is the structure that expands the area of acceptable values of system-wide indicators of national economies
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".