Scientometrics最新文献

This work studies “Contributed” articles in the Proceedings of the National Academy of Sciences of the United States of America (PNAS), a streamlined submission track for members of the US National Academy of Sciences (NAS). We assess the characteristics and impact of those articles and the background and status of their authors, by comparing these articles to PNAS articles following the traditional editorial process. Analyzing over 46,000 articles published between 2007 and 2020, we find: Firstly, and perhaps most centrally, (1) Contributed articles generally appear in lower per-author citation deciles than Direct submissions, but are more likely to appear in the overall top citation deciles of authors; (2) PNAS-Contributed articles tend to spend less time in the review process than Direct submissions; (3) Direct submissions tend to be slightly higher cited than Contributed articles, which are particularly overrepresented amongst least-cited PNAS papers. Disciplinary differences were negligible; (4) authors with lower mean normalized citation scores are profiting most from articles published as Contributed papers, in terms of citation impact; (5) NAS members tend to publish most Contributed articles in the first years after becoming an NAS member, with men publishing more of these articles than women; (6) Contributing authors take up a unique niche in terms of authorship roles, mainly performing supervisory and conceptualisation tasks, without the administration and funding acquisition tasks usually associated with last authors.

Tracing the utilization of science in technological innovations, especially the fraction with regard to public research, is of major importance in science policy. We explore the evolution of the global and domestic technological impact of Chinese scientific output with a detailed analysis of 6,901,428 utility patents granted at USPTO from 1976 to 2020 and their 337,949 citations to Chinese scientific publications. The results show that Chinese scientific output plays an increasingly critical role in science-based innovations while its contributions to domestic and foreign technology are fluctuated over the period. The domestic use of Chinese research is shrinking in late 1990s but keeps increasing thereafter. The technological impact of Chinese scientific output varies in different technology sectors. The recent growing share of Chinese-invented technology in the citing patents is dominated by Chinese patents in digital communication. The time lag of domestic citations is smaller than foreign citations, which is partially owing to the self-citations of Chinese inventors. However, the contributions of self-citations to short knowledge diffusion times are heterogeneous across technology fields. The largest producer of the cited science is universities and the next is public research organizations. Companies account for a meager quantity of total citations and their proportion is shrinking since 2007. Specifically, private technology depends substantially on public research for scientific knowledge. A national bias is found in the scientific knowledge components of patents assigned to companies, which to a certain point indicates the area where academia and industry hold a close relationship in China and Chinese companies are specialized. Taken together, these findings provide a dynamic country- and sector-dependent linkage of Chinese scientific output to domestic and global technology.

Taxonomy alignment is essential for integrating knowledge across diverse domains and languages, facilitating information retrieval and data integration. Traditional methods heavily reliant on domain experts are time-consuming and resource-intensive. To address this challenge, this paper proposes an automated taxonomy alignment approach leveraging large language models (LLMs). We introduce a method that embeds taxonomy nodes into a continuous low-dimensional vector space, utilizing hierarchical relationships within category concepts to enhance alignment accuracy. Our approach capitalizes on the contextual understanding and semantic information capabilities of LLMs, offering a promising solution to the challenges of taxonomy alignment. We conducted experiments on two pairs of real-world taxonomies and demonstrated that our method is comparable in accuracy to manual alignment, while significantly reducing time, operational, and maintenance costs associated with taxonomy alignment. Our case study showcases the effectiveness of our approach by visualizing the taxonomy alignment results. This automated alignment framework addresses the increasing demand for accurate and efficient alignment processes across diverse knowledge domains.

Extreme publishing behavior may reflect a combination of some authors with genuinely high publication output and of other people who have their names listed too frequently in publications because of consortium agreements, gift authorship or other spurious practices. We aimed to evaluate the evolution of extreme publishing behavior across countries and scientific fields during 2000–2022. Extreme publishing behavior was defined as having > 60 full articles (original articles, reviews, conference papers) in a single calendar year and indexed in Scopus. We identified 3191 authors with extreme publishing behavior across science excluding Physics and 12624 such authors in Physics. While Physics had much higher numbers of extreme publishing authors in the past, in 2022 extreme publishing authors was almost as numerous in non-Physics and Physics disciplines (1226 vs. 1480). Excluding Physics, China had the largest number of extreme publishing authors, followed by the USA. The largest fold-wise increases between 2016 and 2022 (5-19-fold) occurred in Thailand, Saudi Arabia, Spain, India, Italy, Russia, Pakistan, and South Korea. Excluding Physics, most extreme publishing authors were in Clinical Medicine, but from 2016 to 2022 the largest relative increases (> sixfold) were seen in Agriculture, Fisheries & Forestry, Biology, and Mathematics and Statistics. Extreme publishing authors accounted for 4360 of the 10000 most-cited authors (based on raw citation count) across science. While most Physics authors with extreme publishing behavior had modest citation impact in a composite citation indicator that adjusts for co-authorship and author positions, 67% of authors with extreme publishing behavior in non-Physics fields remained within the top-2% according to that indicator among all authors with > = 5 full articles. Extreme publishing behavior has become worryingly common across scientific fields with rapidly increasing rates in some countries and settings and may herald a rapid depreciation of authorship standards.

Citation counts are frequently used for assessing the scientific impact of articles. Current approaches for forecasting future citations counts have important limitations. This study aims to analyse and predict the trajectories of citation counts of systematic reviews (SR) based on their citation profiles in the previous years and predict quantiles of future citation counts. We included all SR published between 2010 and 2012 in medical journals indexed in the Web of Science. A longitudinal k-means (KML) clustering approach was applied to identify trajectories of citations counts 10 years after publication, according to the yearly citation count, the proportion of all cites attained in a specific year and the annual variation in citation counts. Finally, we built multinomial logistic regression models aiming to predict in what tercile or quartile of citation counts a SR would be 10 years after publication. Using clustering approaches, we obtained 24 groups of SR. Two groups (7.9% of the articles) had an average of > 200 citations, while two other groups (10.4% of the articles) presented an average of < 10 citations. The model predicting terciles of citation counts attained an accuracy of 72.8% (95%CI = 71.1–74.3%) and a kappa coefficient of 0.59 (95%CI = 0.57–0.62). Prediction of citation quartiles (combining the second and third quartiles into a single group) attained a accuracy of 76.2% (95%CI = 74.7–77.8%) and a kappa coefficient of 0.62 (95%CI = 0.59–0.64). This study provides an approach for predicting of future citations of SR based exclusively on citation counts from the previous years, with the models developed displaying an encouraging accuracy and agreement.

Large bibliographic networks are sparse—the average node degree is small. This does not necessarily apply to their product—in some cases, it can “explode” (not sparse, increasing in temporal and spatial complexity). An approach in such cases is to reduce the complexity of the problem by restricting our attention to a selected subset of important nodes and computing with corresponding truncated networks. Nodes can be selected based on various criteria. An option is to consider the most important nodes in the derived network—the nodes with the largest weighted degree. We show that the weighted degrees in a derived network can be efficiently computed without computing the derived network itself, and elaborate on this scheme in detail for some typical cases.

A standard approach to compare research collaborations between pairs of countries is to look at the citations accrued by all publications with authors from both countries. This approach is often misleading, as aspects only marginally related to the collaboration between the country pairs may bias the result considerably. Among them, the main aspect is the number of co-authors. Publications with many co-authors have on average higher citation impact. If the mix of co-publications between two countries has a high share of such publications, the citation impact will likely be high. Moreover, publications with many co-authors tend to include many countries and are thus only to a limited extent characterising the actual collaboration between the selected pair of countries. The purpose of this study is to develop methods for comparisons of country pairs useful for policy makers, who use SciVal or similar tools. Five methods to compare international collaboration are developed and tested. It is noted that the standard approach for comparisons deviates the most. Fractional methods to calculate the citation impact are recommended, as they allow for the use of citations to all co-publications with a higher weight on the citations to publications in which the country pair dominates. As fractionalisation is laborious to carry out based on SciVal data, a more convenient option is also suggested, which is to use co-publications with maximum 10 co-authors. Elsevier should introduce better methods for comparisons of international collaborations and, until this has been made, help its users understand the limitations of the standard approach featured in SciVal. A by-product of the study is that international co-publications deliver a higher citation impact also when publications with the same number of co-authors are compared.

Amid geopolitical tensions over 5G technology, concerns about foreign firms like Huawei collaborating with academia have surfaced. This paper examines Huawei’s role in Canadian research, analyzing its impact on network robustness and research themes over time. Robustness in network research has been extensively explored, yet there remains a notable gap in understanding the influence of geopolitical factors and foreign corporate presence, such as Huawei’s, on these networks. The main results of this research show that: (1) The 5G network exhibits a decreasing trend in network robustness, with the potential for fragmentation increasing over time; (2) The impact of Huawei’s removal on the network’s Largest Connected Component (LCC) is relatively minor; (3) The network retains its small-world properties irrespective of Huawei’s presence, and its removal has a minor impact on knowledge transfer efficiency; (4) Huawei’s removal does not significantly affect network centralization, nor does it influence the prevailing trend observed over time; (5) Hierarchical clustering and specificity analysis identify Huawei’s strategic focus on the silicon and optical photonic domain within the 5G research; (6) The collaboration-topic network shows a high degree of robustness, suggesting that Canada’s research contributions in these areas are unaffected by the absence Huawei. This study provides a nuanced view of Huawei’s role in Canadian 5G research, suggesting that while the company is a significant player, its impact is in general neither singular nor irreplaceable within the academic network.

The studies of international scientific cooperation have been present in the literature since the early 1990s. However, much less is known about this cooperation at the intercontinental level. Very recently Krawczyk and Malarz (Chaos 33(11):111102, 2023), showed that the rank-based probability distribution of the sequences of ‘continents (number of countries)’ in the authors’ affiliations shows a clear power law with an exponent close to 1.9. In this paper, we focus on the analysis of almost 14 million papers. Based on the affiliations of their authors, we created lists of sequences ‘continent (number of countries)’—at the intercontinental level—and ‘country (number of authors)’ sequences—at the international level—and analysed them in terms of their frequency. In contrast to the intercontinental level, the rank-based probability distribution of the ‘country (number of authors)’ sequences in the authors’ affiliations reveals a broken power law distribution.

In the evaluation of scientific publications’ impact, the interplay between intrinsic quality and non-scientific factors remains a subject of debate. While peer review traditionally assesses quality, bibliometric techniques gauge scholarly impact. This study investigates the role of non-scientific attributes alongside quality scores from peer review in determining scholarly impact. Leveraging data from the first Italian Research Assessment Exercise (VTR 2001–2003) and Web of Science citations, we analyse the relationship between quality scores, non-scientific factors, and publication short- and long-term impact. Our findings shed light on the significance of non-scientific elements overlooked in peer review, offering policymakers and research management insights in choosing evaluation methodologies. Sections delve into the debate, identify non-scientific influences, detail methodologies, present results, and discuss implications.