An 8-phase scientometric, historical, and epistemological audit of the claim that outsiders drive biology

Can academic mentorship be measured? Or will efforts, like a proposed M-Index, lead to further gamification of teaching and learning in postgraduate education?

Background: #health AI investment is accelerating across the Gulf Cooperation Council (GCC), yet the region’s #health AI #research output has never been systematically quantified. National #research capacity is a structural precondition for the safe local development and governance of clinical AI, but no scientometric baseline exists for the 6 GCC states. Objective: We characterized the volume, growth, collaboration structure, and thematic composition of GCC #health AI #research and examined whether national output aligns with an existing measure of #informatics readiness. Methods: We conducted an ecological scientometric analysis of all #health AI publications affiliated with the 6 GCC states (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) for 2011 to 2024 (the last complete indexing year at extraction) using the open bibliographic database OpenAlex. A publication was eligible if it included both an AI family and a #health or medicine family concept above a 0.30 relevance threshold. Bibliometric indicators (output, compound annual growth, per capita intensity, citation impact, international and intraregional collaboration, and institutional concentration) were computed; coauthorship and concept co-occurrence networks were constructed and clustered by modularity. National #health AI intensity was correlated (Spearman) with the #health #informatics Readiness Index (HIRI) and Global #digital #health Monitor (GDHM) phase. Results: Of 112,480 GCC-affiliated AI publications, 3512 met the #health AI definition. Regional output rose from 18 (2011) to 904 (2024) publications per year (compound annual growth=35.2%), with 86.4% (3034/3512) published from 2020 onward and a sharp inflection from 2019 to 2021. Saudi Arabia contributed 59.6% (2094/3512) of the output by itself and 81.5% (2862/3512) together with the United Arab Emirates; Qatar led on a per capita basis (approximately 136 publications per million population). International collaboration (2551/3512, 72.6% of the papers) predominated over intra-GCC collaboration (164/3327, 4.9%), with the latter being inversely related to national output. The principal external partners were India, the United States, Pakistan, the United Kingdom, and Egypt. Output spanned 3186 institutions; concentration ranged from 89.4% (328/367) of a country’s output among the top 3 institutions for Qatar to 34.4% (721/2094) for Saudi Arabia. Concept co-occurrence was classified into 4 themes—classic machine learning clinical prediction, #medical imaging and oncology, #covid19 response, and diabetic eye disease. #health AI intensity (ρ=+0.60) and, more strongly, absolute output (ρ=+0.89) correlated with the HIRI; absolute output was also correlated with GDHM phase (ρ=+0.83). Conclusions: GCC #health AI #research has grown rapidly but unevenly, is heavily dependent on international collaboration, is concentrated in #medical imaging, and—outside Saudi Arabia—remains anchored in single national institutions. #informatics readiness was associated with both the intensity and the volume of national #research output. The findings establish a reproducible baseline for monitoring regional #health AI #research capacity as GCC #health systems move toward clinical AI deployment.

Free Windows app for monitoring publications, citations and h-index across Google Scholar, Scopus, OpenAlex and ORCID, with research analytics and notifications.

Free Windows app for monitoring publications, citations and h-index across Google Scholar, Scopus, OpenAlex and ORCID, with research analytics and notifications.

UCL Discovery is UCL's open access repository, showcasing and providing access to UCL research outputs from all UCL disciplines.

Free Windows app for monitoring publications, citations and h-index across Google Scholar, Scopus, OpenAlex and ORCID, with research analytics and notifications.

Free Windows app for monitoring publications, citations and h-index across Google Scholar, Scopus, OpenAlex and ORCID, with research analytics and notifications.

Responsible scientometrics has become a central concern in the context of increasing reliance on bibliometric indicators for research evaluation and policy decision-making. While existing frameworks such as DORA, the Leiden Manifesto, and CoARA emphasize principles of responsible metrics, less attention has been paid to the underlying data practices that condition the validity, transparency, and reproducibility of scientometric studies. This paper argues that responsible scientometrics cannot be achieved solely through improved indicators or evaluation principles; it also depends critically on the normative structuring of data practices across the entire research lifecycle. We propose a lifecycle-based framework that conceptualizes scientometric research as a sequence of interdependent data practices, including data source selection, data retrieval and acquisition, data preprocessing and standardization, data analysis and visualization, data interpretation, data storage and documentation, and data sharing and citation. For each stage, we identify common methodological risks and propose normative guidelines aimed at improving transparency, reproducibility, and contextual validity. By integrating methodological considerations with principles of responsible research assessment, the framework highlights how data practices are not merely technical procedures but constitutive elements of evaluation outcomes. The proposed framework contributes to scientometrics in three ways: (1) it systematizes scattered methodological recommendations into a coherent lifecycle model; (2) it extends responsible metrics discussions by foregrounding data-level normativity; and (3) it provides a practical reference for researchers, reviewers, and policy stakeholders engaged in research evaluation. Ultimately, this work aims to strengthen the methodological foundations of scientometrics and support more transparent, accountable, and context-sensitive evaluation systems.

The explosive growth of scientometric studies in climate research raises a critical question: do these analyses genuinely advance climate science? An analysis of 703 scientometric climate studies (...

The global science literature, represented as a network with articles as nodes and citations as edges, is a rich artifact for scientometric studies. The structure of this network depends on its count ...

Beberapa bulan lalu di blog ini, saya mengulas bagaimana sains dimanipulasi, mulai dari maraknya paper mill, manipulasi sitasi, jurnal predator, hingga berbagai rekayasa yang membuat publikasi ilmiah tampak jauh lebih produktif dan berpengaruh daripada kenyataan sebenarnya. Laporan Forensic Scientometric (FoSci) tahun 2026 membagi praktik kecurangan ini ke dalam tiga tingkatan mikro, meso, dan makro. Namun, manipulasi ini terus terjadi.