Geomilitary Powers: Evaluation Framework, Spatial Evolution, and Configuration Pathways
Bibliographic record
Abstract
The persistent escalation of great power rivalries and regional conflicts has elevated military factors as increasingly critical variables that shape geopolitical relations and international power dynamics. Conducting an in-depth analysis of the spatial dynamics and formation pathways of geopolitical military influence is therefore of paramount importance for optimizing the allocation of military resources. Building upon the identified gaps in the existing literature on geopolitical military influence, this study employed an index system construction approach that integrated methodologies such as standard deviational ellipse analysis and fuzzy-set qualitative comparative analysis. A preliminary evaluation framework for assessing geopolitical military influence was developed, and its spatial evolution patterns and multidimensional formation mechanisms were examined. The findings revealed the following: (1) Military capability—represented by military financing, personnel strength, and defense industrial production—constitutes the foundational prerequisite for the generation of geopolitical military influence. Military cooperation—encompassing defense collaboration, joint military exercises, and international arms trade—enables the expansion and outward projection of such influence through diverse operational modalities. Military deterrence mechanisms—including nuclear, air-based, and sea-based deterrence—serve as critical manifestations of a nation's strategic acumen and technological prowess, collectively projecting coercive power to deter adversarial states. (2) The global geomilitary landscape demonstrates a stable pattern of "one superpower, many strong powers," with the United States consistently maintaining its position as the most geopolitically influential military power. Concurrently, China and India exhibit significant upward trajectories, whereas Russia and the United Kingdom display declining trends. The dynamic shifts in national geomilitary powers have prompted a southeastward shift in the global power center, with China emerging as the principal driver of this movement. (3) The pathways to geomilitary power can be categorized into three types: synergistic pathways of military strength and cooperation, military cooperation-led pathways, and comprehensive pathways. The multi-condition configurational effects form a robust geomilitary power that cannot be achieved in the absence of core conditions or the presence of only auxiliary conditions. The evolutionary trajectory of a nation's geopolitical military influence progresses through distinct developmental stages—beginning with a military cooperation-led formation pathway, transitioning to a synergistic pathway combining military capability and cooperation, and ultimately ascending toward an integrated pathway. This escalating evolution maps the developmental trajectory through which states enhance their comprehensive national power and geopolitical military influence. This article's in-depth exploration of geopolitical and geomilitary power research not only enhances our understanding of the dynamic evolution of geomilitary power dynamics among different countries but also significantly enriches and expands the research scope of geopolitics and geopolitical military studies. This provides a crucial perspective for comprehending the transformation of the global geopolitical landscape.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".