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Correction: Brachova et al. Codon Composition in Human Oocytes Reveals Age-Associated Defects in mRNA Decay. Int. J. Mol. Sci. 2025, 26 , 9395
In the original publication [...].
Journal Article
Style-free references rather than standardized citation styles
In this communication, the calls for standardizing citation styles are discussed. Instead of standardizing citation style, I consider efforts to introduce style-free references to be more beneficial to authors.
Journal Article
The intellectual structure of the strategic management field: an author co-citation analysis
This paper complements a recent study by Ramos-Rodriguez and Ruiz-Navarro (2004) that investigated the intellectual structure of the strategic management field through co-citation analysis. By using authors as the units of analysis and incorporating all the citations that are included in the Science Citation Index and the Social Science Citation Index, we trace the evolution of the intellectual structure of the strategic management field during the period 1980-2000. Using a variety of data analytic techniques such as multidimensional scaling, factor analysis, and Pathfinder analysis, we (1) delineate the subfields that constitute the intellectual structure of strategic management; (2) determine the relationships between the subfields; (3) identify authors who play a pivotal role in bridging two or more conceptual domains of research; and (4) graphically map the intellectual structure in two-dimensional space in order to visualize spatial distances between intellectual themes. The analysis provides insights about the influence of individual authors as well as changes in their influence over time.
Journal Article
A meta-analysis of semantic classification of citations
The aim of this literature review is to examine the current state of the art in the area of citation classification. In particular, we investigate the approaches for characterizing citations based on their semantic type. We conduct this literature review as a meta-analysis covering 60 scholarly articles in this domain. Although we included some of the manual pioneering works in this review, more emphasis is placed on the later automated methods, which use Machine Learning and Natural Language Processing (NLP) for analyzing the fine-grained linguistic features in the surrounding text of citations. The sections are organized based on the steps involved in the pipeline for citation classification. Specifically, we explore the existing classification schemes, data sets, preprocessing methods, extraction of contextual and noncontextual features, and the different types of classifiers and evaluation approaches. The review highlights the importance of identifying the
citation types for research evaluation, the challenges faced by the researchers in the process, and the existing research gaps in this field.
Journal Article
Updated science-wide author databases of standardized citation indicators
About the Authors: John P. A. Ioannidis * E-mail: jioannid@stanford.edu Affiliations Department of Medicine, Stanford University, Stanford, California, United States of America, Department of Epidemiology and Population Health, Stanford University, Stanford, California, United States of America, Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America, Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, United States of America ORCID logo http://orcid.org/0000-0003-3118-6859 Kevin W. Boyack Affiliation: SciTech Strategies, Inc., Albuquerque, New Mexico, United States of America ORCID logo http://orcid.org/0000-0001-7814-8951 Jeroen Baas Affiliation: Research Intelligence, Elsevier B.V., Amsterdam, the Netherlands ORCID logo http://orcid.org/0000-0001-8005-4153 Citation: Ioannidis JPA, Boyack KW, Baas J (2020) Updated science-wide author databases of standardized citation indicators. [...]we have provided updated analyses that use citations from Scopus with data freeze as of May 6, 2020, assessing scientists for career-long citation impact up until the end of 2019 (Table-S6-career-2019) and for citation impact during the single calendar year 2019 (Table-S7-singleyr-2019). The formula to calculate the composite indicator for career-long impact is derived by summing the ratio of log of 1 + the indicator value over the maximum of those indicator logs for 6 indicators (NC, H, Hm, NCS, NCSF, NCSFL) [3]: The formula to calculate the composite indicator for single year 2019 impact follows the same principle and only uses citations from publications published in 2019.
Journal Article
Comparing examiner citations and applicant citations: insights into technology evolution
Patent citation data is widely used in the study of technology evolution, but existing research has overlooked an issue that there may be potential differences between examiner citations and applicant citations, which may introduce biases from examiner citations. Yet, there is still a lack of systematic comparative study on the differences between applicant citations and examiner citations for technology evolution. To address this, we conducted a comprehensive comparison using USPTO patent data across four dimensions: technology profiling, technology relevance, technology diversity, and technology evolution pathways. For our case study, we selected the promising research area of photovoltaic cells. After comparing nine sub-technologies in this area, we have drawn some conclusions: (1) Applicants tend to provide more citations than examiners, and examiners tend to cite more recent patents than applicants; (2) There is no apparent inclination for applicants to avoid citing particularly relevant patents. On average, examiner citations are slightly closer in technological proximity to their invention than those cited by applicants; (3) The degree of diversity for applicant citations, examiner citations, and applicant & examiner citations at a single patent level lacks consistency. However, their average trend by year or by sub-technology is similar after adding examiner citations; (4) Merging family members strongly impacts main pathways through added examiner citations, which is quite contrary in the citation network with only USPTO-granted patents without merging patent members; (5) In sub-technologies at the growth stage, applicants and examiners both cite more recent patents and tend to integrate border technologies from other fields, which can be used as an indicator for evaluating the potential to become emerging. The findings remind us to pay extra attention to the context in which citation data is used to measure technology evolution, and can serve as signals for technology assessment as well.
Journal Article
Citation bias and other determinants of citation in biomedical research: findings from six citation networks
When the probability of being cited depends on the outcome of that study, this is called citation bias. The aim of this study is to assess the determinants of citation and how these compare across six different biomedical research fields.
Citation network analyses were performed for six biomedical research questions. After identifying all relevant publications, all potential citations were mapped together with the actually performed citations in each network. As determinants of citation we assessed the following: study outcome, study design, sample size, journal impact factor, gender, affiliation, authority and continent of the corresponding author, funding source, title of the publication, number of references, and self-citation. Random effect logistic regression analysis was used to assess these factors.
Four out of six networks showed evidence for citation bias. Self-citation, authority of the author, and journal impact factor were also positively associated with the probability of citation in all networks.
The probability of being cited seems associated with positive study outcomes, the authority of its authors, and the journal in which that article is published. In addition, each network showed specific characteristics that impact the citation dynamics and that need to be considered when performing and interpreting citation analyses.
Journal Article
Closer in time and higher correlation: disclosing the relationship between citation similarity and citation interval
Investigating the intricate relationship between citation similarity and the citation interval offers vital insights for refining citation recommendation systems and enhancing citation evaluation models. This is also a new perspective for understanding citation patterns. In this study, we used the Library and Information Science (LIS) field as an example to determine and discuss the correlation between citation similarity and the citation interval. Using the methods of data collection, paper title preprocessing, text vectorization based on simCSE, calculation of citation similarity and the citation interval, and calculation of the index per citing paper, this study found the following LIS domain-based results: (i) there is a significant negative correlation between citation similarity and the citation interval, but the correlation coefficient is low. (ii) The citation intervals of the least relevant series of cited papers exhibit a more pronounced susceptibility to citation similarity than the most relevant series of cited papers. (iii) The citation intervals of the most relevant cited papers are more concentrated within 12 years and more likely to be published within the average citation interval, typically from the newer half of the cited paper list and published later within 5 years of the citation half-life. This study concludes that researchers usually pay more attention to the latest and most cutting-edge and strongly relevant existing research than to weakly relevant existing research. Continuous attention and timely incorporation of knowledge into the research direction will promote a more rapid and specialized diffusion of knowledge. These findings are influenced by the accelerated dissemination of information via Internet, heightened academic competition, and the concentration of research endeavors in specialized disciplines. This study not only contributes to the scholarly discussion of citation analysis but also lays the foundation for future exploration and understanding of citation patterns.
Journal Article
Understanding the meanings of citations using sentiment, role, and citation function classifications
Traditional citation analyses use quantitative methods only, even though there is meaning in the sentences containing citations within the text. This article analyzes three citation meanings: sentiment, role, and function. We compare citation meanings patterns between fields of science and propose an appropriate deep learning model to classify the three meanings automatically at once. The data comes from Indonesian journal articles covering five different areas of science: food, energy, health, computer, and social science. The sentences in the article text were classified manually and used as training data for an automatic classification model. Several classic models were compared with the proposed multi-output convolutional neural network model. The manual classification revealed similar patterns in citation meaning across the science fields: (1) not many authors exhibit polarity when citing, (2) citations are still rarely used, and (3) citations are used mostly for introductions and establishing relations instead of for comparisons with and utilizing previous research. The proposed model’s automatic classification metric achieved a macro F1 score of 0.80 for citation sentiment, 0.84 for citation role, and 0.88 for citation function. The model can classify minority classes well concerning the unbalanced dataset. A machine model that can classify several citation meanings automatically is essential for analyzing big data of journal citations.
Journal Article