Scientometrics最新文献

Science diplomacy is a unique research field that is driven and shaped by scholars and practitioners alike. This study examines whether and how recent trends in the broader science diplomacy discourse have impacted scholarship on the topic. First, it examines whether the pertinent scholarship is as international in outlook as practitioners have made science diplomacy out to be. Second, the study investigates whether recent calls to diversify the science diplomacy scholarship have gained traction. It does so by examining how diverse the science diplomacy scholarship is in terms of: (i) The geographical distribution of authors, (ii) the geographical distribution of funding sources as well as (iii) the geographical area that is being studied in science diplomacy publications. Using a network analysis and a large language model-enhanced bibliometric analysis, the study shows that the internationalization of the field-both in terms of author affiliations and geographical area being studied in publications-is only slowly advancing and is currently restricted to a few regions, with the United States and Europe clearly dominating the production of knowledge on science diplomacy. Overall, the study's findings thus corroborate past claims that the science diplomacy scholarship exhibits North-South dynamics similar to those in other research fields.

Several computational tools have been developed to detect and identify sexism, misogyny, and gender-based hate speech, particularly on online platforms. These tools draw on insights from both social science and computer science. Given the increasing concern over gender-based discrimination in digital spaces, the contested definitions and measurements of sexism, and the rise of interdisciplinary efforts to understand its online manifestations, a systematic literature review is essential for capturing the current state and trajectory of this evolving field. In this review, we make four key contributions: (1) we synthesize the literature into five core themes-definitions of sexism and misogyny, disciplinary divergences, automated detection methods, associated challenges, and design-based interventions; (2) we adopt an interdisciplinary lens, bridging theoretical and methodological divides across social psychology, computer science, and gender studies; (3) we highlight critical gaps, including the need for intersectional approaches, the under-representation of non-Western languages and perspectives, and the limited focus on proactive design strategies beyond text classification; and (4) we offer a methodological contribution by applying a rigorous semi-automated systematic review process guided by PRISMA, establishing a replicable standard for future work in this domain. Our findings reveal a clear disciplinary divide in how sexism and misogyny are conceptualized and measured. Through an evidence-based synthesis, we examine how existing studies have attempted to bridge this gap through interdisciplinary collaboration. Drawing on both social science theories and computational modeling practices, we assess the strengths and limitations of current methodologies. Finally, we outline key challenges and future directions for advancing research on the detection and mitigation of online sexism and misogyny.

Scientific misconduct and questionable research practices (QRPs) pose significant challenges to the integrity of academic research. This study therefore investigates scientists' implicit associations regarding misconduct and its relationship with perceived academic success. Employing the Single-Category Implicit Association Test (SC-IAT), the attitudes of 11,747 scientists across Austria, Germany, and Switzerland were examined. Results suggest that only a very low fraction of researchers associate serious misconduct with success, yet approximately one-fifth of participants associate QRPs with success. Gender and discipline-based distinctions were minimal, with notable inclinations among PhD students decreasing with status. However, limitations exist, including the lack of validation against explicit attitudes. The decline in the association between QRPs and success with status raises questions about cohort effects or shifting ethical norms. Early intervention and ongoing training efforts may mitigate these issues, emphasizing ethical considerations in scientific practice from undergraduate education onwards. Understanding these dynamics is crucial for fostering integrity in research and guiding future interventions.

Supplementary information: The online version contains supplementary material available at 10.1007/s11192-025-05357-4.

This article reviews Loet Leydesdorff's contributions to science mapping. It explains how over the years, his mapping techniques evolved from journal mapping to global maps of science and finally towards interactive interfaces portraying multiple classifications and ontologies. It then critically reviews the challenges faced by current approaches to science mapping, which implicitly assume a 'natural' epistemic structure, with examples from two recent case studies. We observe that bottom-up algorithmic approaches, either based on citation or semantic approaches, lack conceptual consistency regarding the type of categories used: in a same classification a category captures methods, another one has materials, a third one contains empirical objects and a fourth is focused on theories, rather than having a single logic. I argue that science mapping would produce more useful representations by using ontologies based on a single logic that aligns with the particular conceptual needs of the analysis. Novel classification methods based on machine learning and language models hold promise to produce these tailored, question-driven ontologies.

As an institutional guarantee of technology and innovation, intellectual property rights (IPRs) protection at the national and supranational level has long been an important focus of economics and politics. However, very few studies have examined IPRs protection in the fields of geography and urban studies. Thus, this study aims to investigate IPRs protection within evolutionary economic geography (EEG) by highlighting the effect of economic transition. Taking Huaihai Economic Zone (HEZ), in China, as a sample, the study uses spatial multi-level modelling to better understand the impacts of the threefold process of economic transition (i.e., decentralisation, marketisation and globalisation) on IPRs protection. Our analysis reveals important new insights including: (1) the horizontal spatial distribution of IPRs protection is uneven both horizontally and vertically, and it has significant spatial hotspots; (2) The driving force of China's internal marketisation and decentralisation policy positively influences IPRs protection, unlike in the Global North, because strong IPRs protection is not suitable for the economic conditions of countries in the Global South due to the negative effects of globalisation; (3) Economic transition has a major influence on IPRs protection at the prefectural level, but not at the provincial level. The contributions of the study are twofold: theoretically, it is one of the first paper to examine IPRs protection at the sub-national level within the framework of EEG, and to use the triangular process of economic transformation to explain the resulting institutional changes. Methodologically, based on the theoretical underpinnings of our study, we take different administrative levels and autocorrelation into consideration in our model.

‘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.