Scientometrics最新文献

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.

Besides the highest academic degree with lots of merits post that, getting a Ph.D. and the journey throughout the Ph.D. is not so easy due to which stress and trauma become common among Ph.D. research students. Stress among them can’t be overlooked and is also of major concern as it not only impacts their academic performances but also their mental health, and increases emotional exhaustion. There are many factors that are involved in causing stress among students. Doctoral students are more prone to it as it demands time, selfless effort, and much sacrifice. Moreover, they are in the stage where there are a lot of things going on that distract their minds or sometimes contradict their decisions be it related to their future or to their family, or be it from the institute side. This article mainly deals with analyzing the factors which cause stress, their effects on Ph.D. students, how these factors interrelate with each other, and their percentage share in causing this. Seven dimensions/factors are explored i.e., Institutional Issues, Personal Issues, Supervisor relations, Academic Issues, Fears, Mental Health, and Time Management, which overall depict the entire Doctoral journey. For the analysis of all these dimensions and for finding out the percentage share, a new hybrid method of MCDA (Multi-Criteria Decision Analysis) i.e., fuzzy DEMATEL-ANP with the triangular approach of responses i.e., Optimistic, Pessimistic & Most-Likely is proposed. Performance Analysis and Sensitivity Analysis are done to do the validity check and robustness of the proposed model and by doing this analysis, we identified that the most likely approach in the proposed model is most reliable than the Optimistic and Pessimistic approach due to its non-biased behavior and Supervisor feedback and Uncertain future are the most influential factors and change of city is the least influential one. Moreover, Academic Issues (Poor Writing Skills as well as Publication issues) together with Satisfaction with topic selection during course work period as well as the supervisor's feedback contributes more with weights of 8.1%, 7.7% & 7.5% respectively in causing stress to the doctoral students.

Based on the extracted information of retracted papers from the Retraction Watch Database and the citation information of these papers from the Web of Science, we uncovered the complex relationships of retracted papers in the molecular biology domain via a citation network. The basic characteristics (i.e., time and spatial patterns, reasons, publishers) of the retracted articles were studied. Citation network analysis, including community detection and text analysis, was carried out. Our main findings are as follows: (1) The overall number of retractions in this field has been increasing over time, and these retractions have been mainly in China and the USA. (2) Most retracted papers were for both “scientific error” and “misconduct” reasons. Among the 13 reasons given, errors in the data and images accounted for the largest proportion. (3) Community structure is obvious in the citation network we constructed. In communities with five or more nodes, the average self-citation rate account for 76%. In the three largest communities 1, 2, and 3, the self-citation rate are respectively 99%, 100% and 77%. In community 6, the self-catition rate is 17%. Other papers from different teams were published in the Journal of Cellular Biochemistry (4 papers). Tumor Biology (3 papers) or Febs Letters (1 paper). The self-citation rate of community 5 is 60.00%. Most papers are from Alfredo Fusco’s team, and other ten papers are almost published in PLoS ONE. (4) The coupling relationship between citing-cited retraction reasons was revealed. Retractions and their citations were more likely to be retracted for the same reason. Most of the citing-cited papers from paper mills were published by the same publisher and even the same journal. (5) PI3K (an enzyme), WNT (a protein) and lncRNAs have recently been the major topics of retractions.

Interdisciplinarity is a polysemous concept with multiple, reasoned and intuitive, interpretations across scholars and policy-makers. Historically, quantifying the interdisciplinarity of research has been challenging due to the variety of methods used to identify metadata, taxonomies, and mathematical formulas. This has resulted in considerable uncertainty about the ability of quantitative models to provide clear insights for policy-making. This study proposes a systemic design, grounded in an advanced literature review, to demonstrate that the quantification of the interdisciplinarity of research can be treated as a process of decision-making in mathematical modelling, where alternatives choices are evaluated based on how closely their mathematical properties align with the theoretical objectives of the research design. The study addresses modeling choices regarding the stylisation of metadata into units of observation, and the operational definition of the conceptual dimensions of interdisciplinarity, presenting both established and novel methods and formulas. The final section discusses advanced topics in modelling the measurement, including a dedicated discussion on the difference in analysing the status of papers versus collective bodies of research; and distinguishing between reflective, formative, and inferential causal models of interdisciplinary research.

Academic evaluation is a critical component of research, with the interaction between quantitative and qualitative assessments becoming a prominent area of focus. This study examines the relationship between peer review scores and citations within the framework of open peer review. Utilizing data from the OpenReview platform for papers presented at the International Conference on Learning Representations (ICLR), the papers were classified into oral presentations, poster presentations, and rejected manuscripts. Weighted scores were calculated using the confidence score method, followed by an analysis using correlation and regression techniques. The findings reveal significant differences among the three categories in terms of review scores and citations, demonstrating a positive correlation between review scores and citations. Additionally, it was found that papers with greater inconsistency in reviews tended to receive higher citations. Reviewers of rejected papers displayed significantly higher confidence in their assessments compared to reviewers of accepted papers. The results highlight the alignment between peer review and traditional bibliometric indicators in the context of open peer review. However, the degree of concordance between the two evaluation methods is not substantial, suggesting that they are not interchangeable. Therefore, traditional bibliometric indicators should be considered an essential complement to peer review. Furthermore, when evaluating the consistency between quantitative and qualitative assessments and the confidence levels of reviewers, peer review demonstrates greater effectiveness than “traditional peer review” in addressing issues of “poor selection”.

Understanding citations within their context is a complex task in information science, critical for bibliometric analysis. The study of citation contexts and their types has been a central issue in recent work on citations. In this paper, we present an experiment on the semantic annotation of citation contexts using a rule-based approach. We processed articles from seven PLOS journals and performed semantic annotation of citation contexts based on linguistic resources we constructed. We built on previous work on verb form analysis, n-grams, and semantic category modeling in the form of a linguistic ontology. Based on our observations, we propose directions of work for the constitution of a semantically annotated corpora. The intermediate results obtained lead us to formulate hypotheses on the relation between the IMRaD structure and certain semantic categories. Furthermore, our results demonstrate the semantic richness of citation contexts and underscore the importance of access to full-text articles for ontology population in open science. The findings suggest that semantic categories vary across disciplines and rhetorical structures, necessitating further exploration with larger and more diverse datasets.