Wenceslao Arroyo-Machado

This study presents a multilevel framework to assess the policy attention of academic institutions using Overton as a primary data source. We illustrate this framework through a case study of the University of Granada, combining Overton policy mentions, OpenAlex topics, and bibliometric analysis. Results show policy engagement particularly concentrated in health, physical, and social sciences, with mentions from key governmental and intergovernmental organisations. The study quantifies the university’s visibility in policy documents, classifies mentions by scientific domain and issuer type, maps 1,145 research topics to Sustainable Development Goals (SDGs), and identifies seven researchers whose work aligns with Spain’s Urban Agenda. The findings demonstrate the value of Overton for mapping the presence of institutional research in policy documents and tracing science-policy linkages. However, given the well-documented risks of misuse of bibliometric indicators in research evaluation, users of this framework should be aware of key interpretive boundaries: policy mentions should not be equated with policy impact; raw mention counts cannot establish reliable temporal trends given Overton’s continuous expansion; and geographic and linguistic distributions of mentions reflect Overton’s structural indexing biases toward OECD countries rather than real-world policy engagement. With these caveats, the framework offers a scalable model for institutional policy engagement assessment.

This study presents a multilevel framework to assess the policy attention of academic institutions using Overton as a primary data source. We illustrate this framework through a case study of the University of Granada, combining Overton policy mentions, OpenAlex topics, and bibliometric analysis. Results show policy engagement particularly concentrated in health, physical, and social sciences, with mentions from key governmental and intergovernmental organisations. The study quantifies the university's visibility in policy documents, classifies mentions by scientific domain and issuer type, maps 1,145 research topics to Sustainable Development Goals (SDGs), and identifies seven researchers whose work aligns with Spain's Urban Agenda. The findings demonstrate the value of Overton for mapping the presence of institutional research in policy documents and tracing science-policy linkages. However, given the well-documented risks of misuse of bibliometric indicators in research evaluation, users of this framework should be aware of key interpretive boundaries: policy mentions should not be equated with policy impact; raw mention counts cannot establish reliable temporal trends given Overton's continuous expansion; and geographic and linguistic distributions of mentions reflect Overton's structural indexing biases toward OECD countries rather than real-world policy engagement. With these caveats, the framework offers a scalable model for institutional policy engagement assessment.

Background: A central concern in global health priority-setting is whether the supply of scientific knowledge aligns with health needs and demands. This alignment is usually assessed by comparing research effort with disease burden, overlooking other type of “social demand” of disease, in particular whether diseases are socially visible and generate public attention. We develop an analytical framework that treats public attention and epidemiological burden as complementary dimensions of health demand and examines their alignment with knowledge supply.Methods: We combine data on publications indexed in OpenAlex, disability-adjusted life years from the Global Burden of Disease, and Wikipedia pageviews for 2016 to 2023, as indicators of research effort, disease burden, and public attention, respectively. We map 19 disease groups and 138 specific diseases across these three dimensions. Ternary plots are used to position diseases according to their relative balance across dimensions and to identify diseases that are over- or under-represented in research effort relative to epidemiological burden and public attention. We compare Global North-South patterns using German, Persian, Swahili, and Vietnamese language areas to assess how these relationships vary across territories.Results: The three dimensions show limited alignment. At the disease group level, cardiovascular diseases account for the largest share of disease burden, mental disorders attract the largest share of public attention, and neoplasms concentrate the largest share of research effort. Public attention and disease burden are weakly correlated at both group and specific disease levels, indicating that Wikipedia pageviews and DALYs capture distinct dimensions of health demand. Ternary plots reveal different forms of misalignment, with some diseases showing plots dominated by burden, others by research effort, and others by public attention. Territorial analyses add a further layer by showing that diseases follow disparate patterns of supply-demand (mis)alignment across different linguistic territories.Conclusions: Public attention provides a complementary dimension for mapping global health needs and demands. Our approach identifies where scientific knowledge supply fails to match epidemiological and/or public attention, supporting more nuanced global health analysis that may be useful for priority-setting.

Background: A central concern in global health priority-setting is whether the supply of scientific knowledge aligns with health needs and demands. This alignment is usually assessed by comparing research effort with disease burden, overlooking other type of “social demand” of disease, in particular whether diseases are socially visible and generate public attention. We develop an analytical framework that treats public attention and epidemiological burden as complementary dimensions of health demand and examines their alignment with knowledge supply. Methods: We combine data on publications indexed in OpenAlex, disability-adjusted life years from the Global Burden of Disease, and Wikipedia pageviews for 2016 to 2023, as indicators of research effort, disease burden, and public attention, respectively. We map 19 disease groups and 138 specific diseases across these three dimensions. Ternary plots are used to position diseases according to their relative balance across dimensions and to identify diseases that are over- or under-represented in research effort relative to epidemiological burden and public attention. We compare Global North-South patterns using German, Persian, Swahili, and Vietnamese language areas to assess how these relationships vary across territories. Results: The three dimensions show limited alignment. At the disease group level, cardiovascular diseases account for the largest share of disease burden, mental disorders attract the largest share of public attention, and neoplasms concentrate the largest share of research effort. Public attention and disease burden are weakly correlated at both group and specific disease levels, indicating that Wikipedia pageviews and DALYs capture distinct dimensions of health demand. Ternary plots reveal different forms of misalignment, with some diseases showing plots dominated by burden, others by research effort, and others by public attention. Territorial analyses add a further layer by showing that diseases follow disparate patterns of supply-demand (mis)alignment across different linguistic territories. Conclusions: Public attention provides a complementary dimension for mapping global health needs and demands. Our approach identifies where scientific knowledge supply fails to match epidemiological and/or public attention, supporting more nuanced global health analysis that may be useful for priority-setting.

This study aims to present a comprehensive bibliometric overview of the sport sciences category, focusing on analyzing the multidisciplinary nature of research. The analysis is based on advanced topic modeling developed by the Centre for Science and Technology Studies (CWTS), applied to scholarly outputs from 2001 to 2022 using Clarivate Analytics' Web of Science. Descriptive bibliometric indicators and topic modeling clustered global research into 4,159 topics, including 60,321 outputs specific to sport sciences. The results show significant expansion in sport sciences research, with increased international collaboration and a notable contribution to 1,279 global research topics. Research activity is concentrated, with 75% of output focused on Biomedical and Social Sciences themes.Noteworthy areas include injury prevention, performance optimization, psychological aspects of sports performance, and effective teaching methodologies, highlighting the multidisciplinary nature of the field. These findings provide valuable insights for guiding evidence-based decision-making, shaping science policy, and promoting research diversity and collaboration in sport sciences.

La inteligencia artificial generativa (IAGen) está transformando la universidad, afectando sus funciones esenciales: docencia, investigación, gestión y servicios. La integración masiva de modelos de lenguaje como ChatGPT plantea nuevas oportunidades para automatizar tareas cognitivas, pero también genera retos éticos, epistemológicos y pedagógicos. En la docencia, el uso intensivo de estas herramientas por parte del estudiantado, superior al 85 %, implica una redefinición de las prácticas educativas, ya que comienzan a evidenciarse efectos negativos como la deuda cognitiva o el sedentarismo intelectual. Esto exige nuevas estrategias de alfabetización en IA, evaluación adaptada y formación docente crítica. En investigación, aunque el uso de la IAGen aún no está plenamente extendido, ya cumple funciones en redacción, traducción, análisis y generación de código, aunque su implementación sin supervisión adecuada puede comprometer la integridad científica y la calidad de las publicaciones. El contexto demanda reflexiones rigurosas y propuestas sobre cómo integrar la IA en la academia, objetivo central del monográfico de la revista BiD, que invita a explorar los contornos, riesgos y posibilidades de esta nueva universidad impulsada por IA.

The integration of generative Artificial Intelligence (AI) into academia has sparked interest and debate among academic scientists. This paper explores the early adoption and perceptions of US academic scientists regarding the use of generative AI in teaching and research activities. To do so, this analysis focuses exclusively on STEM fields due to their high exposure to rapid technological advancements. Drawing from a nationally representative survey of 232 respondents, we examine academic scientists’ attitudes, experiences, and intentions regarding AI adoption. Results indicate that 65% of respondents have utilized generative AI in teaching or research activities, with 20% applying it in both areas. Among those currently using AI, 84% intend to continue its application, indicating a high level of confidence in its perceived benefits. AI is most frequently used in teaching to develop pedagogical materials (51%) and in research for writing, reviewing, and editing tasks (40%). Despite concerns about misinformation, with 78% of respondents indicating it as their top concern regarding AI, there is broad recognition of AI’s potential impact on society. Most academic scientists have already integrated AI into their academic activities, demonstrating cautious yet optimistic adoption due to perceived risks. Furthermore, there is strong support for academic-led regulation of AI, highlighting the need for responsible governance to maximize benefits while minimizing risks in educational and research settings.