This study examines the evolution of Ibero-American universities represented in the ARWU-GRAS Communication category between 2022 and 2025, encompassing editions before and after the 2024 methodological revision. Using ARWU-GRAS institutional data and bibliometric records from Web of Science and Journal Citation Reports, the study analyses institutional representation, JCR-Q1 publication profiles, thematic structures, and co-authorship patterns, and compares the pre-reform ARWU-GRAS indicator profiles of institutions that persisted in 2024 with those that exited. Ibero-American representation decreased from 15 universities in 2023 to seven in 2024, while the worldwide total declined from 300 to 200, reducing the Ibero-American share from 5.0% to 3.5%. The same seven institutions remained represented in 2025. Exploratory persistence–exit comparisons showed differentiated pre-reform ARWU-GRAS indicator profiles: TOP provided the clearest differentiation, Q1 showed a substantial difference with greater statistical uncertainty, and CNCI and International Collaboration showed weaker differentiation. Publication, thematic, and co-authorship analyses indicated both continuity and variation across overlapping bibliometric windows. The findings are interpreted as longitudinal associations rather than causal effects. The study contributes to subject-level ranking research by examining institutional persistence and exit across a methodological transition.
This study develops and evaluates a normative policy-weighted, constrained resource-allocation framework, implemented through a composite indicator (C) and a linear allocation rule, to foster open research competence among Ukrainian university staff and align academic publishing with European standards. Using SCImago/Scopus data on open-access (OA) publications by Ukrainian institutions, the number of Ukrainian journals indexed in Scopus, and an author-constructed ordinal index of Open Science training intensity (Tr) for 2020–2025, linear programming was applied to guide resource allocation, and a retrospective reference case decomposition of observed C values was used to interpret changes in the indicator. OA share among Scopus-indexed Ukrainian-affiliated outputs rose from 50.25% (2020) to 63.64% (2025), while Scopus-indexed Ukrainian journals fell from 202 (2022) to 168 (2025). An integral open-research competence indicator increased from 0.088 (2020) to 0.816 (2024) before declining to 0.720 (2025). Under pre-specified policy weights and constraints, the allocation rule yields a corner-solution allocation of 45% to OA support, 35% to Open Science training, and 20% to journal infrastructure. The framework is a transparent decision-support tool for prioritising limited resources; it does not claim causal inference between capacity-building factors and Open Science outcomes.
Retractions are an essential mechanism for correcting the scientific record, yet retraction notices do not always fully explain the circumstances behind the decision. This study assessed the quality of communication in retraction notices published by Latin American journals included in the SCImago Journal & Country Rank up to 2025. Of the 1119 journals examined, we identified 144 retracted articles in Scopus, corresponding to 139 unique retraction notices. The assessment comprised six criteria grouped into two components: access and traceability, and content informativeness and transparency. We also examined several features of the retraction process as reported in the notices. In 77.0% of cases, editorial bodies were the only entities identified in the notices as retracting the article. In 47.5% of cases, the notices did not specify the origin of the alert, request, or process leading to retraction, while 22.3% attributed it to third parties. Only 30.9% explicitly reported an investigation, and 58.3% reported contact with the authors. The mean overall score was 9.15 out of 12 (SD = 1.78). The highest scores were observed for article identification, notice availability, and clarity of the reason for retraction, whereas the lowest were found for specificity of the content or scope affected and transparency regarding the authors’ position. Overall, the findings show that notice availability and article identification alone do not ensure complete reporting of the circumstances surrounding a retraction.
This bibliometric study examines research that explicitly combines sparse methods with Tucker-related techniques. Using PRISMA 2020 as a reporting guide for record identification and selection, we retained 254 journal articles indexed in Scopus and Web of Science and published between 2000 and May 2025. The analysis covers annual publication output, raw citation counts, international collaboration, and keyword co-occurrence using VOSviewer (version 1.6.20) and R (version 4.4.2). The 254 articles show sustained growth in publication output between 2010 and 2024. China and the United States account for a substantial share of the retrieved literature, while Pattern Recognition and IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing are prominent within this specific dataset. Because citation counts vary with publication year, discipline, and database coverage, they are interpreted as descriptive indicators rather than direct measures of research quality or normalized impact. The retrieved studies include applications in neuroscience, finance, hyperspectral image analysis, and multimodal learning. The co-occurrence maps center on terms such as tensor decomposition, low-rank approximation, and deep learning. These results describe how sparse and Tucker techniques are being used across several areas of data science, while remaining specific to the corpus defined by the search strategy.
Large bibliographic databases require careful interpretation because publication trends and subject-area counts are shaped by database coverage, document-type selection, and classification practices. This study examines longitudinal changes in articles and reviews indexed in Scopus between 2010 and 2025, focusing on total Scopus-indexed output, the Business, Management and Accounting (BMA) category, and source-derived subject-area classification multiplicity. A longitudinal bibliometric design combined annual publication counts, growth rates, comparisons between the 2010–2021 baseline and the 2022–2025 recent observation window, exploratory segmented trend models, and the Subject-Area Multiplicity Ratio (SAMR). The SAMR was calculated as the annual sum of source-derived Scopus ASJC subject-area counts divided by the number of unique indexed articles and reviews. It is used as a descriptive ratio of classification multiplicity, not as a measure of article-level interdisciplinarity. The results show sustained but heterogeneous growth across subject areas. BMA recorded comparatively stronger growth during 2022–2025, whereas total Scopus-indexed output did not display a generalized discontinuity after 2022. Generative AI is treated only as contextual background, not as an explanatory factor. The SAMR increased from 1.654 in 2010 to 1.819 in 2025, indicating that summed subject-area counts increasingly exceeded unique-document totals. These findings support cautious interpretation of longitudinal bibliometric indicators in research assessment contexts.
