Scientometrics最新文献

Peer review is a critical process used in academia to assess the quality and validity of research articles. Top-tier conferences in the field of artificial intelligence (e.g. ICLR and ACL et al.) require reviewers to provide confidence scores to ensure the reliability of their review reports. However, existing studies on confidence scores have neglected to measure the consistency between the comment text and the confidence score in a more refined way, which may overlook more detailed details (such as aspects) in the text, leading to incomplete understanding of the results and insufficient objective analysis of the results. In this work, we propose assessing the consistency between the textual content of the review reports and the assigned scores at a fine-grained level, including word, sentence and aspect levels. The data used in this paper is derived from the peer review comments of conferences in the fields of deep learning and natural language processing. We employed deep learning models to detect hedge sentences and their corresponding aspects. Furthermore, we conducted statistical analyses of the length of review reports, frequency of hedge word usage, number of hedge sentences, frequency of aspect mentions, and their associated sentiment to assess the consistency between the textual content and confidence scores. Finally, we performed correlation analysis, significance tests and regression analysis on the data to examine the impact of confidence scores on the outcomes of the papers. The results indicate that textual content of the review reports and their confidence scores have high level of consistency at the word, sentence, and aspect levels. The regression results reveal a negative correlation between confidence scores and paper outcomes, indicating that higher confidence scores given by reviewers were associated with paper rejection. This indicates that current overall assessment of the paper’s content and quality by the experts is reliable, making the transparency and fairness of the peer review process convincing. We release our data and associated codes at https://github.com/njust-winchy/confidence_score.

Coauthor and acknowledgment data were captured for 1384 research articles published between 1980 and June, 2023 that focused on tardigrades. Articles indexed in Web of Science or an archives of tardigrade literature were downloaded and thoroughly examined for personal acknowledgment data. Annual publication counts and coauthor maps for four successive time periods (1980–1999, 2000–2008, 2009–2017, 2018-June 2023) showed growth in the literature and increased research activity (more researchers, more complex networks, more international collaboration), beginning in 2000. A two-level Personal Acknowledgments Classification (PAC), was used to code types of acknowledgments. The majority of articles focused on field studies and/or descriptions of new species of tardigrades. This was reflected in rankings of acknowledgment categories and additions to the PAC. Ranked lists of frequently-thanked acknowledgees (all tardigrade researchers) were produced for each period. Acknowledgment profiles of four frequently-thanked researchers identified three different roles that researchers might play in tardigrade studies—”informal academic editorial consultant,” “taxonomic gatekeeper,” and “all-rounder.” Acknowledgments honoring people by naming a new species after them were only found in the species description, not in the formal acknowledgment section.

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.

Altmetrics have led to new quantitative studies of science through social media interactions. However, there are no models of science communication that respond to the multiplicity of non-academic channels. Using the 3653 authors with the highest volume of altmetrics mentions from the main channels (Twitter, News, Facebook, Wikipedia, Blog, Policy documents, and Peer reviews) to their publications (2016-2020), it has been analyzed where the audiences of each discipline are located. The results evidence the generalities and specificities of these new communication models and the differences between areas. These findings are useful for the development of science communication policies and strategies.

Collaboration networks are widely recognized as essential channels for accessing innovation resources and facilitating creative activities by enabling the exchange of knowledge and information. However, there is little known about whether and how the similarities and dissimilarities between actors forming ties in a collaboration network can either stimulate or inhibit firms’ breakthrough innovation. This study explores the relationship between degree assortativity in collaboration networks and breakthrough innovation performance, considering the moderating role of knowledge network characteristics. Using a sample of 80,129 semiconductor patents from the United States Patent and Trademark Office database spanning the years 1975 to 2007, we constructed both the internal collaboration network and the knowledge network of firms. To test our hypotheses, we employed a negative binomial regression model. Our findings demonstrate that firms with lower degree assortativity in their collaboration networks tend to exhibit higher levels of breakthrough innovation performance compared to those with higher degree assortativity. Moreover, the number of direct ties in the knowledge network strengthens the negative relationship between collaboration network degree assortativity and breakthrough innovation. Conversely, the number of non-redundant ties in the knowledge network mitigates the negative relationship between collaboration network degree assortativity and breakthrough innovation. This study provides practical guidance for firms aiming to enhance their innovation capabilities by simultaneously developing internal collaboration networks and knowledge networks.

The Streetlight Effect represents an observation bias that occurs when individuals search for something only where it is easiest to look. Despite the significant development of Post-Publication Peer Review (PPPR) in recent years, facilitated in part by platforms such as PubPeer, existing literature has not examined whether PPPR is affected by this type of bias. In other words, if the PPPR mainly concerns publications to which researchers have direct access (eg to analyze image duplications, etc.). In this study, we compare the Open Access (OA) structures of publishers and journals among 51,882 publications commented on PubPeer to those indexed in OpenAlex database (#156,700,177). Our findings indicate that OA journals are 33% more prevalent in PubPeer than in the global total (52% for the most commented journals). This result can be attributed to disciplinary bias in PubPeer, with overrepresentation of medical and biological research (which exhibits higher levels of openness). However, after normalization, the results reveal that PPPR does not exhibit a Streetlight Effect, as OA publications, within the same discipline, are on average 16% less prevalent in PubPeer than in the global total. These results suggest that the process of scientific self-correction operates independently of publication access status.

This study aims to identify and compare the national scope of research at the country level, dealing with two groups of countries: Latin America and the Caribbean (LAC) and a group of countries at the forefront in developing mainstream science (WORLD). We wish to explore whether similar or different patterns arise between the two groups at the global and disciplinary level, becoming apparent in their proportion of research related to local perspectives or topics. It is found that Latin America and the Caribbean countries present a greater proportion of local production. The trend to publish national-oriented research is related to disciplinary fields. Even though English is the dominant language of publication, the lingua franca is more likely to appear in the national scope of research, especially for Latin America and the Caribbean countries but also in the rest of non-Anglophone countries. Some implications and limitations for further studies are described.

Altmetrics, or alternative metrics, refer to the newer kind of events around scholarly articles, such as the number of times the article is read, tweeted, mentioned in blog posts etc. These metrics have gained a lot of popularity during last few years and are now being collected and used in several ways, ranging from early measure of article impact to a potential indicator of societal relevance of research. However, there are several studies which have cautioned about use of altmetrics on account of quality and reliability of altmetric data, as they may be more prone to manipulations and artificial inflations. This study proposes a framework based on application of Benford’s Law to evaluate the quality of altmetric data. A large sized altmetric data sample is considered and the fits with Benford’s Law are computed. The analysis is performed by doing plots of the empirical data distributions and the theoretical Benford's, and by employing relevant statistical measures and tests. Results for fit on first and second leading digit of altmetric data show conformity to Benford's distribution. To further explore the usefulness of the framework, the altmetric data is subjected to artificial manipulations through a systematic process and the fits to Benford’s law are reassessed to see if there are distortions. The results and analysis suggest that Benford’s Law based framework can be used to test the quality of altmetric data. Relevant implications of the research are discussed.

The study utilized Latent Dirichlet Allocation (LDA) Topic modeling to identify prevalent latent topics within Open Access (OA) Library and Information Science (LIS) journals from 2013 to 2022. Eight core OA Scopus indexed journals were selected based on their SJR scores and DOAJ listing. Titles, Abstracts and keywords of 2589 articles were extracted from the Scopus database. R software packages were used to perform data analysis and LDA topic modeling. The optimal value of k was determined to be 9. The analysis revealed that 53.89% of documents comprise over 50% of a certain topic (θ > 0.50). Notably, ‘Scholarly Communication’ and ‘Information Systems, Models and Frameworks’ emerged as dominant topics with the highest proportions of research literature in the corpus. The topic ‘Scholarly Communication’ experienced significant growth with an average annual growth rate (AAGR) of 4.37%, while ‘Collection development and E-resources’ exhibited the lowest research proportion and a negative AAGR of − 4.22%. Additionally, topics such as ‘Information Seeking Behaviour and User Studies’, ‘User Education and Information Literacy’, and ‘Information Retrieval and Systematic Review’ remained stable and persistent topics. Conversely, research on traditional topics like ‘Librarianship and Profession’, ‘Bibliometrics’ and ‘Medical Library and Health Information’ showed a gradual decline. The LDA topic modeling approach unveiled previously unknown or unexplored topics in open access LIS research literature, enhancing our understanding of emerging trends.

The standard and fractional projections are extended from binary two-mode networks to weighted two-mode networks. Some interesting properties of the extended projections are proved.