Catégories
FR

Étude et classification des contextes de citation pour une construction d’indicateurs relationnels sémantiques

Author : Yutong Fei

Depuis la première proposition d’une typologie de catégories sémantiques citationnelles en 1965 par Eugene Garfield, pour mieux comprendre les raisons et les fonctions de l’acte de citer, ce sujet de recherche n’a cessé de susciter l’intérêt de plusieurs disciplines, telles que la sociologie des sciences, la linguistique, la bibliométrie et l’informatique.

Dans cette perspective, les travaux empiriques ont évolué, passant des entretiens sur les motivations de citation à la classification sémantique des contextes de citation, c’est-à-dire les passages textuels entourant une référence dans un article scientifique.

Avec le développement des approches informatiques, cette classification tend aujourd’hui à s’automatiser, donnant lieu à des services à valeur ajoutée intégrés dans les systèmes de recommandation de citations destinés à améliorer la recherche bibliographique.

Or, comprendre la citation suppose d’abord de préciser ce que recouvre la sémantique citationnelle et d’en saisir la genèse. Celle-ci ne se réduit pas à une analyse linguistique, mais reflète les stratégies scientifiques, sociales, rhétoriques, et discursives des auteurs. Les formes de citation résultent ainsi de multiples facteurs, tels que les spécificités disciplinaires, les pratiques de recherche ou encore l’évolution des normes éditoriales au fil du temps. Réduire les contextes de citation à de simples ressources d’entraînement pour des applications industrielles appauvrit la compréhension de la citation, en négligeant les tensions qui structurent cette complexité.

Cet enjeu est au cœur de cette thèse, qui construit un corpus de contextes de citation prenant en compte les facteurs influençant la production du discours citationnel, notamment la diversité disciplinaire et éditoriale, afin de proposer une typologie des différentes manières de citer.

Le corpus réunit 8 358 contextes issus de 157 revues internationales, publiées entre 2020 et 2023, période marquée par une tendance à la standardisation des politiques éditoriales autour des valeurs d’ouverture et de transparence de la recherche. En complément de cette analyse discursive, une étude exploratoire fondée sur des entretiens semi-directifs avec trois acteurs phares du marché de la citation a été réalisée pour interroger l’usage industriel des contextes de citation.

Les 50 catégories de citation dégagées dans cette thèse révèlent une grande diversité sémantique, façonnée par la tension entre les caractéristiques disciplinaires du discours citationnel et la standardisation éditoriale. Les différentes manières de construire le savoir propre à chaque discipline se reflètent dans cette diversité, telle qu’elle s’exprime à travers la dimension discursive de la citation, car la construction du savoir et celle du discours sont indissociables, particulièrement dans les SHS.

Par ailleurs, la structuration de cette diversité discursive par les lignes éditoriales constitue un enjeu industriel, en lien avec la future exploitation des contextes de citation dans les bases de données. Les résultats de cette recherche doctorale montrent les effets des cadres politiques et institutionnels de la recherche sur les cultures discursives de la citation.

De même, ils montrent de quelle manière l’implémentation des contextes de citation ouvre la voie à de nouvelles formes d’exploitation de la publication scientifique ouverte, qui offre aux acteurs du marché des opportunités renouvelées de marchandisation.

URL : https://hal.science/tel-05687014v2

Catégories
EN

Research quality metrics as a complement to bibliometrics: an opinion paper

Authors : Harald Hannerz, Kristian Schultz Hansen, Jan Hyld Pejtersen, Helene Kless Willadsen

Substantial proportions of research investment may be wasted due to flawed study design, biased reporting, and publication bias, raising concerns about how research performance is evaluated.

Despite this, current assessment systems rely heavily on bibliometric indicators such as publication counts, co-authorship, and citation metrics, which do not reliably reflect research quality or scientific rigor.

In this opinion paper, we argue that such indicators may reward practices that undermine the accumulation of reliable knowledge, including p-hacking, selective reporting, and underpowered study designs, while undervaluing essential contributions such as replication research.

We propose that bibliometric measures should be complemented by transparent and auditable research quality metrics, including indicators of reproducibility, statistical power, risk of bias, and adherence to reporting standards.

We further suggest that recent advances in large language models may facilitate the scalable implementation of such metrics. Adopting quality-focused indicators could improve research evaluation and help reduce avoidable research waste.

URL : Research quality metrics as a complement to bibliometrics: an opinion paper

DOI : https://doi.org/10.1007/s11192-026-05840-6

Catégories
EN

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

Catégories
EN

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

Catégories
EN

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

Catégories
EN

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

Catégories
EN

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