Modeling the Spatial Development of the US Cross-Border Regions as Mesolevel Socio-Economic Systems
Bibliographic record
Abstract
The cross-border nature of the spatial development of economically and socially significant regions identified in the geospace of the largest federal states of the modern world necessitates their study from the standpoint of the mesoeconomics of development as complex, open and dynamic systems in space-time. The object of this study is the cross-border regions of the United States with Mexico and Canada, which are distinguished by a characteristic set of specific properties of both system-structural and socio-economic nature. According to the systemic economic theory, the identified cross-border regions of the United States show the principle of duality of spatial development, and within the framework of evolutionary theory – a specific “socio-economic genotype” of the territory. Using elements of mesoeconomic modeling, the author identified and substantiated some trends in the spatial development of different groups of cross-border regions of the United States over a long time period of 1970–2020 and as a forecast until 2030. Based on a comparative analysis carried out on the results of modeling and testing, the author's hypothesis about a multidirectional change in the dynamics of indicators of socio-economic spatial development, which depends on the regional features of the spatiotemporal evolution of specific transboundary territories, is confirmed – as socio-economic systems of the mesolevel. The obtained results, revealing the spatial dependence of the data, indicate the growth of spatial relationships of both social and economic nature within the studied cross-border regions of the United States, which determine the spatiotemporal evolution of the latter in the geoeconomic and geocultural space of the United States and neighboring states. Based on the results of mesoeconomic modeling, the existence of an evolutionary-cyclical nature and duality of the studied mesosystems is empirically confirmed, having their own socio-economic genotype of territories, depending on the insurmountable specifics of spatial diversity and unevenness in the conditions of the market organization of American society. The main conclusion is that regional interest, which has dialectical unity and variability, manifested over time, causes different directions and levels of spatial development of specific regions of the United States as integral dynamic and open socio-economic systems of the mesoterritorial level, reflecting the gradual increase in economic regionalization. The approach used, taking into account the specifics of spatial development, is of interest for the study of similar subject issues in relation to large federal states that have a transboundary nature of genetically determined socio-economic interaction.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".