Scientometrics最新文献

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.