Étiquette : Bibliometrics

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Artificial Intelligence in Scientific Publishing: Citation Impact and Thematic Trends

Author : Mohammadamin Erfanmanesh

Introduction: The rise of artificial intelligence (AI) has a significant impact on scientific publishing and librarianship. This study explores the citation impact and thematic patterns of AI-related research in scientific publishing through four objectives: (1) to analyze bibliometric characteristics; (2) to evaluate citation performance using normalized metrics; (3) to identify major thematic trends; and (4) to review AI-related publications that specifically address librarianship and examine their implications for library practices.

Methods: A bibliometric analysis was conducted on 295 AI-related publications indexed in the Web of Science Core Collection. Citation performance was assessed using two field-normalized metrics from Clarivate’s InCites platform: the Category Normalized Citation Impact (CNCI) and the Journal Normalized Citation Impact (JNCI). To determine whether the median CNCI and JNCI values differed significantly from the category and journal benchmarks, a one-sample Wilcoxon signed-rank test was performed. Sub-themes within AI-related research were identified through thematic analysis. Additionally, to highlight important themes and professional implications, publications that specifically mention libraries, librarians, or librarianship were reviewed.

Results: The median CNCI was 2.7, and the median JNCI was 2.2, both significantly above the benchmark of 1.0 (P < .001). This indicates that AI-related research in scientific publishing receives significantly more citations than comparable publications within the same subject areas and journals. Seven sub-themes were identified, all showing above-average citation impact. Among these, themes focused on frameworks, policies, and ethical guidelines for AI integration achieved the highest normalized citation scores. Additionally, four publications focusing specifically on librarianship explored how AI intersects with open access and scholarly communication, ethics and governance, professional training, the development of machine-readable collections, and the integration of AI technologies into library services.

Discussion: These findings suggest that AI-related research has rapidly emerged as a highly influential area within scientific publishing. The variety of themes demonstrates the broad impact of AI technologies on scientific publishing.

Conclusion: Research on AI in scientific publishing is a rapidly growing and dynamic area, notable for its high citation impact, broad thematic scope, and growing influence on libraries and librarianship.

URL : Artificial Intelligence in Scientific Publishing: Citation Impact and Thematic Trends

DOI : https://doi.org/10.31274/jlsc.20363

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Comparison of OpenAlex and Scopus coverage of German institutions’ publications in top-tier journals

Authors : Andrey Lovakov, Ivan Sterligov

OpenAlex has recently emerged as a leading alternative to proprietary bibliometric sources. However, concerns remain regarding the quality of its metadata, especially the institutional profiles which are crucial for evaluating organizations. This study assesses the quality of affiliation data in OpenAlex using German research institutions.

Publications from top-tier journals were analyzed and institutional publication counts in OpenAlex were systematically compared with counts in Scopus. The results show that OpenAlex generally contains more publications at the journal level, reflecting its broader coverage. However, institutional publication counts in OpenAlex are consistently lower, indicating missing or incorrectly assigned affiliations.

Nevertheless, the correlations between institutional outputs in both databases are very high, suggesting that relative institutional rankings remain stable. These findings suggest that OpenAlex is suitable for comparative institutional analyses in academic research but requires further improvement in affiliation metadata before it can be used for evaluation contexts that rely on absolute publication counts.

DOI : https://doi.org/10.48550/arXiv.2605.01337

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Author Name Disambiguation in Scholarly Research: A Bibliometric Perspective

Authors : Hesham Amin Hamdy El Shamly, Subaveerapandiyan A.

The rapid expansion of scholarly publishing has amplified the long-standing challenge of author name ambiguity in academic databases. This issue, manifesting as homonymy and synonymy, undermines the accuracy of bibliometric analyses, author-level metrics, and research evaluation systems. Author Name Disambiguation (AND) has thus emerged as a critical focus area in digital scholarship, with evolving strategies ranging from supervised machine learning and graph-based models to the adoption of persistent digital identifiers like ORCID.

Despite notable advancements, significant challenges remain – particularly in linguistically diverse and underrepresented regions – where metadata inconsistencies, transliteration issues, and limited ORCID adoption exacerbate disambiguation errors. This study presents a comprehensive bibliometric analysis of 2,004 publications on AND from 2005 to 2024, sourced from the Scopus database.

Using tools such as Biblioshiny and VOSviewer, the analysis identifies publication trends, leading authors and institutions, core sources, co-authorship networks, and thematic evolution in the field. Findings highlight increasing international collaboration, the dominance of computer science-driven methodologies, and the critical role of metadata quality and institutional frameworks.

The study concludes with recommendations for inclusive, multilingual, and interoperable disambiguation systems, advocating for cross-disciplinary collaboration to ensure equitable author identification in global scholarly communication.

URL : Author Name Disambiguation in Scholarly Research: A Bibliometric Perspective

DOI : https://doi.org/10.1515/opis-2025-0035

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Using bibliometrics to detect questionable authorship and affiliation practices and their impact on global research metrics: A case study of 14 universities

Authors : Lokman I. Meho, Elie A. Akl

From 2019 to 2023, a subset of 80 highly published universities demonstrated research output increases exceeding 100%, compared to the global average of 20%. Among these, 14 institutions showed significant declines in first authorship rates, raising questions about their authorship and affiliation practices.

This study employed bibliometric analysis to examine shifts in authorship and affiliation dynamics at these universities. Key findings include a 234% rise in total publications, a 23 percentage point drop in first authorship rates, and an increase in hyper-prolific authors from 23 to 177. International collaborations surged, and several universities exhibited sharp rises in multiaffiliated publications. Additionally, the proportion of articles published in top 10% journals increased by 11 percentage points, and the proportion of articles ranked among the world’s top 10% most cited grew by 12 percentage points.

These trends raise concerns about the integrity of authorship and affiliation practices as they deviate from normative behavior, far exceeding those observed nationally and at top-ranked universities—Caltech, MIT, Princeton, and UC Berkeley.

The study emphasizes the need for collaborative reforms by universities, ranking agencies, publishers, and other entities, highlighting the importance of each entity’s role in preserving academic integrity and ensuring the reliability of global research metrics.

URL : Using bibliometrics to detect questionable authorship and affiliation practices and their impact on global research metrics: A case study of 14 universities

DOI : https://doi.org/10.1162/qss_a_00339

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Patent research in academic literature. Landscape and trends with a focus on patent analytics

Authors : Cristian Mejia, Yuya Kajikawa

Patent analytics is crucial for understanding innovation dynamics and technological trends. However, a comprehensive overview of this rapidly evolving field is lacking. This study presents a data-driven analysis of patent research, employing citation network analysis to categorize and examine research clusters. Here, we show that patent research is characterized by interconnected themes spanning fundamental patent systems, indicator development, methodological advancements, intellectual property management practices, and diverse applications.

We reveal central research areas in patent strategies, technological impact, and patent citation research while identifying emerging focuses on environmental sustainability and corporate innovation. The integration of advanced analytical techniques, including AI and machine learning, is observed across various domains. This study provides insights for researchers and practitioners, highlighting opportunities for cross-disciplinary collaboration and future research directions.

URL : Patent research in academic literature. Landscape and trends with a focus on patent analytics

DOI : https://doi.org/10.3389/frma.2024.1484685

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The use of ChatGPT for identifying disruptive papers in science: a first exploration

Authors : Lutz Bornmann, Lingfei Wu, Christoph Ettl

ChatGPT has arrived in quantitative research evaluation. With the exploration in this Letter to the Editor, we would like to widen the spectrum of the possible use of ChatGPT in bibliometrics by applying it to identify disruptive papers.

The identification of disruptive papers using publication and citation counts has become a popular topic in scientometrics. The disadvantage of the quantitative approach is its complexity in the computation. The use of ChatGPT might be an easy to use alternative.

URL : The use of ChatGPT for identifying disruptive papers in science: a first exploration

DOI : https://doi.org/10.1007/s11192-024-05176-z

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On The Peer Review Reports: Does Size Matter?

Authors : Abdelghani Maddi, Luis Miotti

Amidst the ever-expanding realm of scientific production and the proliferation of predatory journals, the focus on peer review remains paramount for scientometricians and sociologists of science. Despite this attention, there is a notable scarcity of empirical investigations into the tangible impact of peer review on publication quality.

This study aims to address this gap by conducting a comprehensive analysis of how peer review contributes to the quality of scholarly publications, as measured by the citations they receive. Utilizing an adjusted dataset comprising 57,482 publications from Publons to Web of Science and employing the Raking Ratio method, our study reveals intriguing insights. Specifically, our findings shed light on a nuanced relationship between the length of reviewer reports and the subsequent citations received by publications.

Through a robust regression analysis, we establish that, beginning from 947 words, the length of reviewer reports is significantly associated with an increase in citations. These results not only confirm the initial hypothesis that longer reports indicate requested improvements, thereby enhancing the quality and visibility of articles, but also underscore the importance of timely and comprehensive reviewer reports.

Furthermore, insights from Publons’ data suggest that open access to reports can influence reviewer behavior, encouraging more detailed reports. Beyond the scholarly landscape, our findings prompt a reevaluation of the role of reviewers, emphasizing the need to recognize and value this resource-intensive yet underappreciated activity in institutional evaluations.

Additionally, the study sounds a cautionary note regarding the challenges faced by peer review in the context of an increasing volume of submissions, potentially compromising the vigilance of peers in swiftly assessing numerous articles.

HAL : https://cnrs.hal.science/hal-04492274