Scientometrics最新文献

‘The wisdom of crowds’ theory has received widespread attention and application. For scholars, the wisdom of crowds is of great significance in revealing the operating mechanism of the scientific community. However, scholar crowds are jointly affected by scientific cognition and coordination, which are different from general human crowds. ‘The wisdom of crowds’ theory poses significant challenges in terms of directly explaining and evaluating the wisdom generation among scholars. Considering that knowledge diffusion is an important way to generate scientific cognition and coordination, this work proposed ‘the wisdom of scholar crowds’ and evaluates it from the perspective of knowledge diffusion. First, scholar-paper and scholar-topic two-layer networks were constructed, achieving a holistic representation of scientific coordination and cognition in the network structure dimension. Second, the topic consistency among scholars was identified using the two-layer networks, and a knowledge diffusion evaluation model based on topic consistency was designed to evaluate the scale and threshold of the wisdom generation of scholar crowds. Finally, combined with 3,838,048 paper data, this work revealed that the cohesion and bridging of network structure contribute to the wisdom generation of scholar crowds. By comparing with the commonly used evaluation methods, this study shows that the generating difficulty of the wisdom of scholar crowds will be underestimated without topic consistency. This work provides a new perspective for expanding the ‘wisdom of crowds’ theory and a novel method for evaluating knowledge diffusion and the wisdom of scholar crowds.

Both computational social scientists and scientometric scholars alike, interested in gender-related research questions, need to classify the gender of observations. However, in most public and private databases, this information is typically unavailable, making it difficult to design studies aimed at understanding the role of gender in influencing citizens’ perceptions, attitudes, and behaviors. Against this backdrop, it is essential to design methodological procedures to infer the gender automatically and computationally from data already provided, thus facilitating the exploration and examination of gender-related research questions or hypotheses. Researchers can use automatic gender detection tools like Namsor or Gender-API, which are already on the market. However, recent developments in conversational bots offer a new, still relatively underexplored, alternative. This study offers a step-by-step research guide, with relevant examples and detailed clarifications, to automatically classify the gender from names through ChatGPT and two partially free gender detection tool (Namsor and Gender-API). In addition, the study provides methodological suggestions and recommendations on how to gather, interpret, and report results coming from both platforms. The study methodologically contributes to the scientometric literature by describing an easy-to-execute methodological procedure that enables the computational codification of gender from names. This procedure could be implemented by scholars without advanced computing skills.

Research into the mobility of researchers has garnered increasing interest among institutions and governments. In this study, we use ORCID as a data source to analyse the mobility of researchers trained in Germany, France, Spain, the Netherlands, and Italy, the main economies of the Eurozone according to Gross Domestic Product (GDP). Our approach focuses on the connection between the place of education and employment, identifying graduates and their countries of employment through profiles on ORCID. We conduct a comparative analysis of preferred destinations, considering various levels of education, and develop a migration rate for researchers from these countries. The results reveal a clear preference for the United States and Great Britain among graduates, influenced by linguistic affinities and historical cultural relations. Regarding the migration rate, we observe that all countries retain more graduates than those who emigrate. France leads in emigration, followed by the Netherlands, Germany, Italy, and Spain. This analysis of researcher mobility in the Eurozone allows us to track migratory flows, identifying both sending and receiving countries. These findings are essential for the formulation of scientific and migration policies and contribute to understanding individual behaviour in building academic and professional careers.

Graphical abstracts (GAs) are publication extenders used to visually communicate scientific concepts and data alongside their parent manuscript. This study investigated the prevalence and characteristics of GAs published in a clinical pharmacology journal that facilitates GA use through free publication and providing templates to authors. The characteristics of clinical publications in the British Journal of Clinical Pharmacology in issues dated 2021–2023 were collated and accompanying GAs reviewed and compared with the associated written abstracts. In total, 64/1019 (6.3%) publications were accompanied by a GA. There was no association between the presence of a GA and the geographical location of the principal investigator, year of publication or open access status. Industry-funded studies were significantly more likely to include a GA compared with non-industry funded studies (19/179 [10.6%] vs. 25/458 [5.5%]; Fisher’s exact test, p = 0.0246). Professional medical writing support was also associated with a numerically higher prevalence of GAs (16.7% [11/66] vs. 7.6% [7/92] with no medical writing support; Fisher’s exact test, p = 0.1257). While GAs generally included study results (94%), only approximately half presented methodology (58%) and conclusions (50%). Few GAs (27%) included the title of the publication. In conclusion, uptake of GAs by authors was low. Industry and professional medical writing support was associated with increased GA uptake, but the prevalence remained below 20%. GAs are also heterogenous in nature, often inconsistent with the written abstract and are generally unable to stand alone.

It is a common phenomenon for scientists to follow hot topics in research and this phenomenon can generally be quantified by measuring the preference attachment of new papers. A similar phenomenon also exists when a paper chooses its references. However, the abovementioned method does not apply to measure the preference for hot papers. To solve this problem, in this paper, we propose to convert measuring a paper’s preference for hot papers into calculating the hotness obtained from a paper’s references. We propose a PageRank-like algorithm that considers the hotness propagation based on citation relationships between papers to measure the hotness transfer of individual papers. We apply this method to the American Physical Society journals and explore the hotness transfer performance of individual papers in physics. It is found that highly innovative papers, such as Nobel Prize-winning papers in physics, have a weaker hotness transfer degree than papers with the same number of citations. We explore the factors associated with the performance of hotness transfer indicators. We find that the larger the size or citation counts of the field are, the stronger the hotness transfer degree of the field is likely to be. The team size and the number of references can also affect the hotness transfer degree of individual papers. Finally, we find that the hotness transfer scores of papers show an increasing trend over time. Relevant empirical discoveries may be valuable for evaluating paper impact.

The assessment of scientific papers has long been a challenging issue. Although numerous studies have proposed quantitative indicators for assessing scientific papers, these studies overlooked the citation characteristics and the novelty of scientific knowledge implied in the textual information of papers. Therefore, this paper constructs an integrated indicator to evaluate scientific papers from both citation and semantic perspectives. Firstly, we propose weighted citations to measure the academic impact of scientific papers, which takes time heterogeneity and citation sentiment factors into consideration. Secondly, we capture the novelty of scientific papers from a semantic perspective, utilizing FastText to represent papers as text embeddings and applying the local outlier factor to calculate it. To validate the performance of our approach, the bullwhip effect domain and the ACL Anthology corpus are used for case studies. The results demonstrate that our indicator can effectively identify outstanding papers, thus providing a more comprehensive evaluation method for evaluating academic research.

Measuring disruptive innovation is a critical and still-developing topic. Although the disruption (D) Index has been widely utilized, it ignores the structural differences between i- and j-type nodes and suffers from inconsistencies, biases related to reference lists, and little comparability across different clusters. To address these possible biases, we propose the improved disruptive Index (ID Index), using a dataset of 114,202 patents from Chinese listed firms to test its validity. The results show that the ID Index (i) provides a more precise measurement of disruptiveness, resolves inconsistencies, reduces biases related to reference lists, and enhances comparability across clusters; (ii) demonstrates better convergent validity, correlating more closely with expert evaluations and more effectively identifying determinants such as knowledge search, recombination, and coordination; (iii) shows better validity in predicting stock market reactions, renewal durations, firms’ short- and long-term performance. Finally, we separate the ID index to independently measure the extent of disrupting and consolidating existing knowledge, and the convergent and predictive validity are demonstrated.

Enhancing the effectiveness and efficiency of national research systems is a top priority on the policy agendas of many countries. This study focuses on one aspect of the macroeconomic efficiency of research systems: whether research institutions specialize in scientific domains where they have a competitive advantage. To evaluate this, we developed a novel methodology. This methodology measures the scientific specialization indices of each organization in various research fields and assesses their relative research productivity. It then examines the correlation between these scores and between the resulting rankings. We applied this methodology to Italian universities. We found that a significant rank correlation between universities’ field specialization and their performance appears only in a few areas, and overall, the rankings are completely unrelated. Providing such data to research managers and policymakers can help inform strategies to enhance both micro- and macro-level efficiency.

In science and beyond, quantifications are omnipresent when it comes to justifying judgments. Which scientific author, hiring committee-member, or advisory board panelist has not been confronted with page-long publication manuals, assessment reports, evaluation guidelines, calling for p-values, citation rates, h-indices, or other numbers to judge about the ‘quality’ of findings, applicants, or institutions? Yet, many of those of us relying on and calling for quantifications may not understand what information numbers can convey, and what not. Focusing on the uninformed usage of bibliometrics as worrisome outgrowth of the increasing quantification of science, in this opinion essay we place the abuse of quantifications into historical contexts and trends. These are characterized by mistrust in human intuitive judgment, obsessions with control and accountability, and a bureaucratization of science. We call for bringing common sense back into scientific (bibliometric-based) judgment exercises. Despite all number crunching, many judgments—be it about empirical findings or research institutions—will neither be straightforward, clear, and unequivocal, nor can they be ‘validated’ and be ‘objectified’ by external standards. We conclude that assessments in science ought to be understood as and be made as judgments under uncertainty.

Decades after the fall of the Iron Curtain, medical research in Russia remains poorly integrated into global science. In this study, we analyze the evolution of Russian medical research presence in international journals in recent years and examine the role of international collaboration in driving this change. We collected data from various sources, including Web of Science, Scopus, and Medline. While articles in international journals still constitute a smaller proportion of all Russian medical publications, their representation has significantly increased in recent years. Articles in high-impact journals now comprise approximately one-third of the total output. International cooperation emerges as a key factor behind top-level Russian medical publications, with international coauthorship playing a particularly significant role in high-impact journals, where 70% of Russian-authored publications include foreign co-authors. It is noteworthy that Russian authors are rarely designated as corresponding authors, suggesting a limited leadership role in project teams, especially, regarding research published in the most prestigious publications. Additionally, Russian scientists produce a notably low number of non-collaborative papers that later achieve high citation rates. Given that 81% of the most productive authors participate in international projects and the recent suspension of many ties and collaborations with foreign scientists, we expect a significant decline in Russia’s presence in core medical journals in the near future.