https://hdl.handle.net/10481/18658 Sat, 11 Apr 2026 12:24:20 GMT 2026-04-11T12:24:20Z https://hdl.handle.net/10481/110904 How to achieve gender parity in science? Providing global evidence on key educational and economic drivers González-Salmón, Elvira; Chinchilla-Rodriguez, Zaida; Robinson García, Nicolás; Nane, Gabriela F. Gender parity in science depends on a complex interplay of social, economic and educational variables. In this study, we compile a longitudinal dataset at the country level combining scientific bibliographic data from Dimensions, with the World Bank Open Data (WBOA), and the UNESCO Institute for Statistics (UIS). Our goal is to identify conditions and pathways that could lead to gender parity in different world regions, by applying time-series forecasting methods (ARIMA and Exponential Smoothing), along with correlation analysis and Bayesian networks. While results vary by region, one recurring recommendation emerging from our models is the need to increase the number of researchers and the percentage of women graduating in Engineering, Manufacturing, and Construction, as this appears to be a critical driver for reducing gender disparities in the scientific workforce. https://hdl.handle.net/10481/110904 https://hdl.handle.net/10481/110902 Inferring Gender from Author Names with Local LLMs: A Multi-Model Evaluation Herrero Solana, Víctor; González-Salmón, Elvira; Robinson García, Nicolás Gender identification of researchers is a common practice in scientometric studies examining inequalities in science. The most widely used approach relies on inferring gender from author names using commercial APIs or name-gender dictionaries, which often lack transparency and reproducibility. This study explores the use of local open-weight Large Language Models (LLMs) as an alternative for name-based gender classification. We evaluate 25 models from seven leading families (Llama, Gemma, Phi, Mistral, Qwen, DeepSeek, and Yi), ranging from 270 million to 70 billion parameters, using a reference dataset of nearly 200,000 names across 195 countries extracted from Wikidata. Results show that top-performing models achieve F1-Scores above 0.93 for both gender categories, positioning local LLMs as a viable, cost-effective, and reproducible alternative to proprietary tools. A critical performance threshold emerges at approximately 7 billion parameters, above which all models achieve acceptable results, with diminishing returns beyond 12-14 billion. All models exhibit systematic gender bias, showing higher precision for men and higher recall for women, indicating a tendency to classify ambiguous names as male. Mistral-Nemo-12b emerges as the optimal choice, balancing accuracy, computational efficiency, and gender equity. https://hdl.handle.net/10481/110902 https://hdl.handle.net/10481/107064 The OpenAlex database in review: Evaluating its applications, capabilities, and limitations Forchino, María Veronica; Torres Salinas, Daniel Abstract: This systematic review examines OpenAlex, focusing on its technical features and its applications within science studies and scientific information sources. The paper offers a critical perspective on OpenAlex’s role in democratizing and promoting equity in access to scientific information. A total of 146 articles published between 2022 and 2025 are analyzed and classified by functionality and application, including AI-based validation processes. Findings reveal the rapid adoption of OpenAlex as an open alternative to commercial databases, particularly across the Global South. However, notable metadata limitations—especially regarding affiliations, languages, and document types—still affect analytical reliability. The study highlights key opportunities and challenges for researchers, libraries, institutions, and the technical community, emphasizing the need for complementary validation and responsible assessment strategies. Maria Veronica Forchino carried out this research with funding from the 2025 International Postdoctoral Mobility Call, between institutions belonging to the Ibero-American University Association for Postgraduate Studies (AUIP), as part of “Action Line 3: Mobility Grants for Postgraduate Studies” of the AUIP 2024-2026 Action Plan. Junta de Andalucía. https://hdl.handle.net/10481/107064 https://hdl.handle.net/10481/105806 Enseñar e investigar con inteligencia artificial: una llamada a la reflexión Torres Salinas, Daniel; Arroyo Machado, Wenceslao https://hdl.handle.net/10481/105806 https://hdl.handle.net/10481/104881 Análisis de cobertura de referencia entre OpenAlex y Web of Science en artículos en coautoría ArgentinaEspaña (2013-2023) Fuente-Gutiérrez, Enrique; Kippes, Romina El presente trabajo analiza la evolución de las coautorías entre España y Argentina a partir de un análisis de 47.354 artículos científicos multidisciplinares publicados entre 2013 y 2023. Teniendo en cuenta las diferentes prácticas de publicación y circulación del conocimiento entre ambos países, y el crecimiento en sus publicaciones de acceso abierto, se comparan dos bases de indexación comerciales –Scopus y Web of Science– con OpenAlex, que aspira a posicionarse como una referencia de búsquedas bibliográficas más inclusiva. Del estudio se desprende que OpenAlex incluye un 27.9% más registros que Web of Science y 29,3% más que Scopus; además de una presencia mayor de artículos de ciencias sociales y humanas en comparación con las dos bases comerciales, en las que predominan disciplinas como la química y la inmunología. Del análisis, además, sobresale el rol de las universidades y organismos científicos en la producción y circulación del conocimiento, a partir de un análisis de afiliaciones y casas editoriales. Los datos sugieren que la inclusión de OpenAlex permite una visión más diversa de la producción científica y de las colaboraciones internacionales, especialmente en disciplinas y regiones geográficas tradicionalmente subrepresentadas.; This article analyzes the evolution of co-authorship between Spain and Argentina based on an analysis of 47,354 multidisciplinary scientific articles published between 2013 and 2023. Taking into account the different publication practices and circulation of knowledge between both countries, and the growth in their open access publications, two commercial indexing bases –Scopus and Web of Science– are compared with OpenAlex, which aspires to position itself as a more inclusive reference for bibliographic searches. The study shows that OpenAlex includes 27.9% more records than Web of Science and 29.3% more than Scopus; in addition to a greater presence of articles from social sciences and humanities compared to the two commercial databases, in which disciplines such as chemistry and immunology predominate. The analysis also highlights the role of universities and scientific organizations in the production and circulation of knowledge, based on an analysis of affiliations and publishing houses. The data suggest that the inclusion of OpenAlex allows for a more diverse view of scientific production and international collaborations, especially in traditionally underrepresented disciplines and geographic regions. https://hdl.handle.net/10481/104881