Muestra la distribución de disciplinas para esta publicación.
Publicaciones WoS (Ediciones: ISSHP, ISTP, AHCI, SSCI, SCI), Scopus, SciELO Chile.
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| DOI | |||||
| Año | 2023 | ||||
| Tipo | artículo de investigación |
Citas Totales
Autores Afiliación Chile
Instituciones Chile
% Participación
Internacional
Autores
Afiliación Extranjera
Instituciones
Extranjeras
Objective: This study explores the intricate interplay between Cuba's placement on the state sponsors of terrorism list and its impact on scientific collaboration with the United States, specifically in Health Sciences. Design/Methodology/Approach: Using a robust Poisson regression framework, the investigation examines collaboration dynamics in two scenarios: when Cuba is designated a terrorism sponsor (group 1) and when it's not (group 0). Results/Discussion: Outcomes reveal the pivotal role of Cuba's terrorism listing in shaping collaboration between nations. A negative coefficient for "Inclusion" indicates a 69% reduction in expected joint scientific articles when Cuba is listed as a terrorism sponsor. Conclusions: These findings highlight the interplay between geopolitical factors and scientific partnerships, with implications for policymakers and researchers. They underscore the need to consider broader consequences of such designations. Originality/Value: By shedding light on the complex convergence of science and diplomacy, this research underscores the urgency of fostering productive scientific discourse. It propels Health Sciences towards progress, even in a world of diplomatic shifts. These insights guide collaborative efforts in Health Sciences, emphasizing the importance of navigating challenging geopolitical landscapes.
| Ord. | Autor | Género | Institución - País |
|---|---|---|---|
| 1 | Ronda-Pupo, Guillermo Armando | Hombre |
Universidad Católica del Norte - Chile
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| Agradecimiento |
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| Each individual journal and paper were methodically assigned to a distinct field or subfield within the health sciences domain using the Science Metrix journal classification. This classification methodology closely aligns with the NSF (National Science Foundation) journal classification system. Through a hybrid approach that combines algorithmic techniques and expert evaluation, each journal and its associated papers were meticulously matched to a single, mutually exclusive domain, field, or subfield. |