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.
Artificial intelligence is reshaping scientific research, yet its contribution may be associated with whether methods are matched to the types of research problems. This study investigates this question in medical informatics using 40,107 articles published during 2000–2024. Large language models are used to identify three AI method families—traditional machine learning, neural network methods, and generative AI—while a knowledge-distilled classifier groups studies into three problem types: comprehension-oriented, solution-oriented, and exploration-oriented. The study then examines how different problem–method combinations are associated with citation impact and recombinatorial novelty. AI use increased sharply after 2018 and became increasingly embedded in medical informatics. All three methods are positively associated with citation impact, with relatively stronger associations for generative AI. Associations vary by problem type: all three methods are positively associated with impact in solution-oriented studies; neural networks and generative AI are positively associated with impact in comprehension-oriented studies, whereas neural networks are negatively associated with impact in exploration-oriented studies. Traditional machine learning is positively associated with novelty in comprehension-oriented and solution-oriented studies, whereas neural networks are positively associated only in solution-oriented studies. These findings suggest that AI’s contribution to science is associated with specific problem–method combinations and that broad claims about AI’s role can obscure heterogeneity across research tasks.
Diamond Open Access journals face increasing challenges in ensuring metadata quality, interoperability, persistent identification, digital preservation, and international visibility while operating with limited financial and technological resources. Although Open Journal Systems (OJS) provides a robust publishing platform, the absence of an integrated technological governance model can hinder the systematic coordination of scholarly communication infrastructures. This study proposes and evaluates a Technological Governance Framework for Diamond Open Access journals using a Design Science Research methodology. The framework is organized into five governance dimensions: metadata management, persistent identifiers, scholarly interoperability, digital preservation, and editorial technologies. To support the assessment of technological governance performance, five composite indicators were integrated into a Technological Governance Index (TGI). The framework was evaluated through its implementation in the Innovación y Software journal, demonstrating its practical feasibility within a real Diamond Open Access publishing environment. The implementation achieved high levels of metadata quality, persistent identifier adoption, preservation, and interoperability, with a TGI of 97.43%. The journal also received the inaugural Crossref Metadata Excellence Award in the New Members category in 2025, providing complementary evidence of the metadata quality achieved in the evaluated case study. Although the results demonstrate the feasibility of the proposed governance model within the evaluated institutional context, further multi-journal studies are required to assess its transferability and broader applicability. The proposed framework provides a structured and measurable basis for coordinating technological capabilities in Diamond Open Access publishing and for future research on technological governance, metadata quality, interoperability, and digital sustainability.
Scientific output in health sciences has grown exponentially, yet its landscape remains under-analyzed in emerging economies. In Peru, Microbiology and Parasitology are disciplines of strategic relevance for public health, but no specific bibliometric analysis exists. This study provides the first baseline to identify thematic gaps and characterize research patterns within the SciELO Peru collection. Through a descriptive bibliometric analysis of 549 Microbiology and Parasitology articles indexed in the SciELO Peru collection (2000–2023), temporal, regional, and institutional indicators, methodological design, and journal characteristics were evaluated using R v4.4.0. Results show that production peaked in 2021, coinciding with the COVID-19 pandemic period and accompanied by a marked increase in Virology-related publications. Lima accounted for 77.3% of the SciELO Peru corpus national output, with UPCH (17.5%), INS (14.9%), and UNMSM (13.7%) leading institutional activity. Bacteriology was the dominant area (44.1%), while Mycology was the most underrepresented (2.4%), and cross-sectional designs predominated (63.9%). Notably, real international collaboration was limited to 1.8%, and RPMESP was the sole Bradford core journal, accounting for 48.5% of the corpus, with no articles appearing in Q1 journals. These findings reveal that while production shows sustained growth, it is marked by high geographic concentration and a scarcity of applied technological research within the analyzed corpus. These findings highlight the urgent need to decentralize research and reorient Peru’s health science agenda toward critical, under-studied disciplines.
In a time where the methods of science are frequently called into question, open science practices are gaining increased importance. A particularly cooperative form of transparent science is the approach of adversarial collaboration (AC), where research groups with different explanations of the same phenomenon work closely together to develop a study and publish their results jointly, regardless of the outcome. The aim of this work, a systematic mapping review, is to provide a comprehensive overview of the formal aspects of AC and identify the relevant factors that need to be considered when applying this method. For this purpose, both AC studies and publications related to this topic were analyzed. The total of 89 articles retrieved, analyzed, and integrated indicates that accounts on this topic have slightly increased (mostly in psychology) in recent years. However, only a handful of authors have delved more deeply into ACs, and there currently is no journal specifically promoting ACs. The analyzed sources provide a solid foundation for best-practice recommendations and a comprehensive overview of the pros and cons of AC, in the form of organized lists of AC-related known advantages and challenges (alongside solutions to the latter ones). Specifically, some AC studies show weaknesses in study quality, mainly due to a lack of information on the implementation steps. This systematic mapping review serves not only as an introduction to the topic, but also as a guide for future ACs.
This article describes the design, development, and user acceptance evaluation of the Monitor CRIS UCSM, a research monitoring system (RMS) built on the Elsevier Pure REST API for the Universidad Católica de Santa María (UCSM), Arequipa, Peru. The study addresses two research questions: (RQ1) can an API-based CRIS monitoring solution be implemented on a commercial Research Information Management System (RIMS) to meet institution-specific regulatory requirements, and (RQ2) to what extent do institutional stakeholders perceive such a system as usable, normatively relevant, and useful for strategic decision-making? The system integrates bibliometric data from Pure into dashboards covering research production metrics, RENACYT-qualified researcher profiles, SDG alignment, and regulatory compliance indicators aligned with Peruvian higher education legislation (Law No. 30220, SUNEDU, CONCYTEC), implemented on a three-tier architecture (Laravel 11/Vue.js 3/MySQL 8.0) with role-based access control. A User Acceptance Test (UAT) conducted with 134 institutional stakeholders, using an instrument adapted from the System Usability Scale (SUS) and structured into four dimensions, showed excellent internal consistency (α = 0.90), a composite SUS-adapted score of M = 81.12 (SD = 5.83), and very favorable ratings across all four dimensions (global M = 4.32; SD = 0.14 on a 1–5 Likert scale). These findings, bounded to the UCSM institutional case, support the technical feasibility of API-based CRIS monitoring within a single institution. The decision-making utility reported here reflects internal stakeholder perceptions gathered through a single UAT administration, not measured organizational outcomes, external adoption, or longitudinal use.
This paper proposes a level-based scientific maturation master plan (SMMP) for strengthening research projects prior to manuscript submission. A weak manuscript is often not simply a weak text but an immature project that has been translated too early into publication form. Contemporary research management is better at registering deadlines, deliverables, resources, and visible publication signals than at diagnosing the internal maturity of a scientific object. The result is false readiness: a project may have a topic, structure, literature, methodological vocabulary, and a polished manuscript but still lack a mature problem, a coherent conceptual architecture, a testable design, sufficient evidence, and a disciplined contribution. To address this gap, this paper proposes a nine-level SMMP, moving from thematic impulses to peer review and publication readiness. The model integrates noncompensatory gates, evidence packages, red flags, maturation debt, bottlenecks, the publication maturation gap, and peer-review readiness. Methodologically, the paper is a conceptual design study supplemented by a proof-of-concept documentary application to publicly available CORDIS project biographies. The framework shows how to distinguish publication polish from scientific maturation and how to translate expert criticism into concrete presubmission actions.
Quartile rankings of journals have become shorthand for research quality in many national evaluation systems. This Commentary offers a non-systematic documentary analysis of this phenomenon in Spain and selected Latin American systems. It conceptualizes these arrangements as quartile regimes: configurations of rules, indicators, organizational routines, and incentives that make the Journal Citation Reports or SCImago quartile position of a journal function as a high-stakes proxy for research quality. The article draws on legal and policy texts, agency criteria, reform documents, peer-reviewed literature, and selected integrity cases used as illustrative vignettes rather than prevalence evidence. Spain is analyzed as an early and influential case in which sexenios and accreditation made journal indicators central to individual careers, although the 2024 sexenio criteria now move explicitly toward qualitative narratives, broader outputs, and responsible indicators. Mexico, Brazil, Colombia, Argentina, and Peru are treated as purposive Latin American cases that show distinct pathways through individual recognition schemes, graduate-program evaluation, journal-indexing systems, career committees, and publication bonuses. The article argues that quartile regimes reshape publication language, research agendas, disciplinary hierarchies, authorship practices, and integrity risks, with particularly strong effects in the social sciences and humanities and in regional journal ecosystems. Current reform efforts, including the Agreement on Reforming Research Assessment, the Coalition for Advancing Research Assessment, FOLEC-CLACSO, and the ALAEC manifesto, show that quartiles can be repositioned as weak contextual signals within broader, field-sensitive frameworks that value quality, bibliodiversity, multilingual communication, open science, and societal relevance.
There is contested use of Google Scholar as a primary database for PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) reviews. However, well-cited articles identify Google Scholar as insufficiently reliable and evaluate its use as supplementary. Subsequent systematic review searches have accepted this relegation of Google Scholar to supplementary status, citing these articles as the reason. This study questions this acceptance by (1) revealing the type of difficulties with Google Scholar identified in these well-cited publications compared with PRISMA guidelines, and (2) examining several PRISMA scoping review primary database searches performed by this author since 2023 for the adequacy of Google Scholar results compared with them. The results reveal that the reasons for considering Google Scholar a supplementary database regarding PRISMA status are not convincing, as they are unrelated to PRISMA guidelines for systematic reviews. Google Scholar returned the greatest number of included studies for the majority of post-2023 scoping reviews conducted by this author. These results demonstrate that the accepted advice to authors that Google Scholar should be a supplementary database is unsupported. Based on the results of this research, the suggestion is to accept Google Scholar as a primary database, comparable in all relevant ways to other primary databases for a PRISMA-style review.
Rejection of review invitations due to time constraints is putting pressure on the peer review system, showing that less time-consuming ways of reviewing are needed. This paper presents the results of a field experiment with a new format for grant peer review and answers the question of whether this new format is less time-consuming while still providing high-quality reviews. In the new approach, the Peer Circle (PC), a team of reviewers collectively evaluates several grant applications. The PC was applied to four fields and compared with four similar fields using conventional peer review. Qualitative and quantitative methods have been used to analyze heterogeneous data such as interviews with and a survey of the peer reviewers; text analysis of the review reports; and statistical analysis of bibliometric applicant data. The comparison suggests that the PC saves time and enlarges the reviewer population considerably. Most reviewers felt that the quality of the PC evaluations was at least as good as that of the conventional evaluations, if not better. Given these findings, the experiment is now continued on a much larger scale. Apart from that, the theoretical implication is that the way of organizing peer review has an important effect on the functioning of the system.
Bibliometric data from various databases are crucial for exploring research trends through a bibliometric analysis. Usually, deduplicating records and merging several citation index databases for bibliometric research is tedious, particularly when dealing with larger datasets. Although several manual and automatic merging processes are available in the academic literature, some key issues were identified during the implementation of existing merging processes. To address such issues, this paper proposes an open-source preprocessing pipeline developed using R programming for a simple merging of bibliometric data collected from multiple databases. This open-source reproducible preprocessing pipeline precompiles and deduplicates records based on a Digital Object Identifier (DOI). To implement this proposed research work, bibliometric data are considered from Scopus, Web of Science and Lens databases. The key outcomes of this research work are identifying multiple DOIs and Titles, standardizing the DOIs, and deduplicating records to obtain a merged dataset without noisy data. This enables researchers to conduct an effective bibliometric analysis.
The rapid advancement of artificial intelligence (AI) is reshaping the landscape of library and information science, significantly altering the roles and responsibilities of information professionals. This paper aims to examine the transformations of information professional roles in the era of artificial intelligence. This study conducted a systematic literature review (SLR) emulating the PRISMA 2020 protocol. Titles and abstracts were screened based on predefined inclusion criteria, including English full-text journal articles, review papers, and conference papers indexed in Scopus addressing the roles and competencies of information professionals in the era of artificial intelligence. The study employed a conceptual and review analysis of documents to examine the use of AI and its impact on the roles of information professionals. We investigated the positive and negative effects of AI on the roles of information professionals, as well as the evolving role of information professionals in routine process automation. AI’s presence and transformation of virtually all the information professionals’ roles are profound, with pertinent challenges. The impact of AI on the roles of information professionals are both positive and negative, while the roles of information professionals have undergone significant changes in the AI era. This paper presents a unique perspective on the evolving roles of information professionals in the era of artificial intelligence. It offers original insights into how AI is reshaping the profession, highlighting the profound impacts and transformations that are redefining traditional practices and skill sets.
The rapid integration of generative artificial intelligence into scientific publishing is reshaping how academic text can be produced, revised, and scaled. While transparent and limited use of AI for language support may be acceptable, a new structural vulnerability may be emerging in medical publishing: the large-scale production of short, plausible, and weakly individualized correspondence across multiple specialties. In this viewpoint, we describe and conceptualize a pattern that may be termed synthetic authorship, defined not as undisclosed AI use alone, but as a reproducible mode of scholarly output structurally facilitated by automation. We focus particularly on letters to the editor, a format that combines brevity, rapid editorial handling, and formal indexation, and may therefore be especially exposed to this phenomenon. Based on recurring patterns observed in PubMed-indexed literature, including unusually high publication velocity, abrupt thematic dispersion, and stylistic uniformity across unrelated domains, we argue that such outputs may challenge the authenticity, epistemic value, and editorial function of scientific correspondence. We do not present empirical proof of misconduct, but rather outline a conceptual framework for understanding this emerging risk and propose proportionate editorial safeguards, including cross-domain pattern detection and contextual assessment of authorship coherence. As AI lowers the threshold for generating domain-plausible commentary at scale, scientific publishing must adapt its integrity frameworks accordingly. In this context, vigilance toward synthetic authorship may become an essential component of editorial responsibility and post-publication quality control.
Literature reviews are essential for synthesizing existing knowledge, mapping research domains, identifying intellectual structures, and highlighting research gaps within a field. However, many literature reviews are incomplete because database search strategies are not adequately specified or validated. Search strategies are frequently underreported and undermotivated across the systematic review literature and bibliometrics, while query formulation remains time-consuming, error-prone, and particularly difficult in interdisciplinary or rapidly evolving topics. This article fills that void by developing a guideline for designing a professional topic query in existing academic databases and emphasizing search design as the front-end validity problem in bibliometric research. The article uses the Artificial Intelligence Think Tank framework as a methodological engine and applies it to bibliometric retrieval engineering via structured interaction with generative AI systems and human experts. The paper assists scholars performing bibliometric studies, scientometric analyses, systematic literature reviews, scoping reviews, and hybrid evidence-synthesis projects.
With the rapid advancement in Generative Artificial Intelligence (GenAI), AI-generated content (AIGC) lacking human cognitive oversight is increasingly permeating open web environments and academic communication systems. This study integrates longitudinal retraction data (Retraction Watch Database, 1990–2026), web-scale analyses of AI-content penetration (Common Crawl, 2013–2026), and bibliometric mapping of governance scholarship (Web of Science Core Collection, Scopus, Google Scholar, 2020–2026) to diagnose the cross-level misalignment between synthetic-content diffusion, AI-related misconduct pressure, and governance attention. On this basis, it proposes a Normalized Coverage Index (NCI) to measure the relative relationship between scholarly attention to AI-related academic misconduct governance and the level of misconduct pressure observed through retraction data across disciplines. The results reveal pronounced asymmetries at the disciplinary level. Fields such as chemistry (0.04), physics, mathematics & statistics (0.11), and life sciences & biology (0.34) exhibit clear governance gaps, whereas Education shows a comparatively excessive level of attention (NCI = 29.26). Since 2022, AIGC has expanded rapidly across open web corpora, accompanied by a sharp rise in AI-related retractions, which also exhibit a longer detection lag than traditional forms of misconduct (2.77 years vs. 1.91 years). Although the volume of academic governance-related research has grown rapidly, its proportion within the broader body of AI-related research has declined, suggesting that scholarly attention to governance has not kept pace with technological diffusion. Consequently, a structural misalignment in governance—closely tied to the allocation of attention—has emerged within the academic system in the era of GenAI. This misalignment may pose potential risks to the robustness of the knowledge production system. Addressing it requires rebuilding epistemic infrastructure through provenance transparency, auditable workflows, and governance-aware seed corpora aligned with empirically concentrated risks.
Large language model (LLM) chatbots, as a widely used form of generative artificial intelligence, have reduced the marginal cost of producing publication-style manuscripts and have expanded feasible routes for manipulating citation metrics within the publishing ecosystem. Citation-based indicators (e.g., the h-index, the i10-index, and total citation counts) remain embedded in research evaluation and are sensitive to indexing practices of bibliographic databases, with Google Scholar providing broad coverage combined with comparatively limited curation. In this study, a systematic literature review is conducted to synthesize reported mechanisms of citation-metric manipulation and to examine limitations of citation-metric use, including evidence reported in civil engineering. A Google Scholar proof-of-concept case study examines whether the indexing of LLM-assisted, non-peer-reviewed documents with concentrated references to a target author is associated with changes in author-level citation metrics under platform-specific conditions. After indexing, a stepwise increase in author-level metrics is observed, demonstrating the feasibility of citation-metric manipulation under the platform-specific conditions. Finally, this paper discusses the implications for research integrity and citation manipulation in the era of generative artificial intelligence. It also presents recommendations for researchers, academic institutions and evaluation committees, publishers and editors, bibliographic database providers, and funding institutions and policymakers.
Annotation and indexing of classical Chinese medical texts enable the extraction of core information, facilitating structured annotation and standardised indexing. These processes provide essential support for knowledge retrieval, digital utilisation, and in-depth analysis of these texts. Recent advances in digital technologies have opened new possibilities for annotation and indexing, offering transformative approaches to address challenges arising from the abstract, concise, and complex nature of classical Chinese medical literature. However, existing research has largely overlooked the intrinsic interconnection and synergistic mechanisms between annotation and indexing within the workflow. This study examines annotation and indexing as an integrated whole, reviewing the current state of research, identifying existing challenges, and proposing future directions. The findings reveal that major challenges in the annotation and indexing of classical Chinese medical texts centre on four key areas: cultural connotation, rule formulation, result dissemination, and technical algorithms. Addressing these issues requires a systematic approach, including the development of a cultural heritage framework for Traditional Chinese Medicine, the establishment of standardised annotation and indexing principles, the construction of high-quality corpora, the optimisation of data circulation mechanisms, and the refinement of intelligent algorithms. Advancing the annotation and indexing of classical Chinese medical texts not only promotes their efficient circulation and secondary utilisation but also lays a solid foundation for the large-scale mining of Traditional Chinese Medicine knowledge, its modern transmission, and cross-disciplinary intelligent applications, thereby driving the innovative development of the field.
The diversification of research outcomes produced within scholarly communication practices has led to a growing production of non-traditional research outputs (NTROs) such as datasets, software, databases, exhibitions, and multimedia materials, which are often poorly tracked by institutional systems. This study presents an exploratory study about knowledge production at the University of Bologna (UNIBO). This prominent national research institution offers a compelling case study to assess coverage, cross-repository overlap, and citation activity of NTROs across repositories. By harvesting and integrating metadata from the University of Bologna’s institutional CRIS system, the institutional repository AMS Acta, the general-purpose repository Zenodo, the disciplinary archive Software Heritage, and OpenCitations to gather citation information, we analyse the availability of UNIBO-affiliated NTROs and show that, while the UNIBO CRIS platform (i.e., IRIS) remains the primary registry for information on NTROs, a substantial number of them are hosted exclusively in external repositories. These findings highlight structural gaps in tracking NTROs in UNIBO IRIS and underline the need for improved interoperability and coordinated Open Science strategies and policies, at least at the local level, to ensure recognition of diverse research outputs.
The BRICS countries are playing an increasingly significant role in shaping a multipolar model of global science. This study aims to assess the academic influence of economic journals published in BRICS countries from the following key perspectives: academic standing, relevance, influence sustainability, internationalization, and external institutional recognition (lack of isolation). The methods of bibliometric, comparative, and cluster analysis were used. The study revealed that the BRICS countries have significantly increased their presence in the Scopus database. However, their scientific publishing landscape is highly heterogeneous. Russia and India exhibit the highest publication volumes among the BRICS countries, albeit with relatively low citation rates and a low level of internationalization. Meanwhile, Chinese, South African, and Indonesian journals have the highest citation rates and strongest integration into the global discourse. Cluster analysis identified five groups of journals with a range of academic influence levels, from peripheral contributors to international leaders. Additionally, country-specific features of their distribution were determined. The present research provides insights into the pivotal role of national journals in overcoming peripherality and strengthening the academic influence of nationwide science. The research methodology can be used to develop strategies that promote nations to become part of the global research community.
Government funding programs administered by the Australian Research Council (ARC) aim to advance national research priorities while generating scholarly and socio-economic impact. This study employs a descriptive bibliometric benchmarking approach to examine the relationship between funding levels and scholarly output for publications explicitly acknowledging ARC support. Using project-level funding data linked with journal articles published between 2009 and 2016, we analyze 10,565 ARC-funded projects receiving a total of AUD 4.6 billion and producing 54,639 journal publications. On average, each project received approximately AUD 437,720 and generated five publications, corresponding to a cost of about AUD 84,700 per article. We compare research productivity, citation impact, and return on investment across ARC Discovery and Linkage programs, as well as between STEM and HASS disciplines. The results reveal no strong correlation between funding amount and either publication volume or citation impact across ARC programs. STEM projects generally exhibit higher returns on investment and citation impact; however, a subset of HASS projects achieves exceptionally high efficiency relative to funding received. Notably, projects funded below AUD 100,000 demonstrate the highest return on investment in terms of both publication productivity and normalized citation impact. These findings suggest that smaller grants can yield disproportionately high scholarly returns, offering important implications for research funding allocation, efficiency evaluation, and performance assessment in public research systems.
