Geomilitary Powers: Evaluation Framework, Spatial Evolution, and Configuration Pathways
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,011 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».