Journal of Informetrics最新文献

Against the backdrop of increasing transparency in scientific publications and the complexity of citation motivations, the applicability and efficacy of open peer review (OPR) remain controversial. Utilizing a dataset of citations and altmetrics for all articles published in Nature Communications and PloS One, in this study the impact of OPR is investigated from the dimensions of open review reports and open identity reviewers. The analysis reveals articles subjected to OPR have no obvious advantage in citations but a notable higher score in altmetrics. The distribution of data variation across most disciplines, displaying a statistically significant difference between OPR and non-OPR, mirrors the overall trend. Two potential explanations for the disparity in OPR's impact on citations compared to altmetrics are proposed. The first relates to the quality heterogeneity between OPR and non-OPR research, while the second is related to the diverse authors citing and mentioning articles in distinct communities. This study's findings carry policy implications for future OPR practices.

This study investigates how scientific research influences policymaking by analyzing citations of research articles in policy documents (policy impact) for nearly 125,000 articles across 434 public policy journals. We reveal distinct citation patterns between policymakers and other stakeholders like researchers, journalists, and the public. News and blog mentions, social media engagement, and open access publications (excluding fully open access) significantly increase the likelihood of a research article being cited in policy documents. Conversely, articles locked behind paywalls and those published under the full open access model (based on Altmetric data) have a lower chance of being policy-cited. Publication year and policy type show no significant influence. Our findings emphasize the crucial role of science communication channels like news media and social media in bridging the gap between research and policy. Interestingly, academic citations hold a weaker influence on policy citations compared to news mentions, suggesting a potential disconnect between how researchers reference research and how policymakers utilize it. This highlights the need for improved communication strategies to ensure research informs policy decisions more effectively. This study provides valuable insights for researchers, policymakers, and science communicators. Researchers can tailor their dissemination efforts to reach policymakers through media channels. Policymakers can leverage these findings to identify research with higher policy relevance. Science communicators can play a critical role in translating research for policymakers and fostering dialogue between the scientific and policymaking communities.

Semantic representation methods play a crucial role in text mining tasks. Although numerous approaches have been proposed and compared in text mining research, the comparison of semantic representation methods specifically for publication keywords in bibliometric studies has received limited attention. This lack of practical evidence makes it challenging for researchers to select suitable methods to obtain keyword vectors for downstream bibliometric tasks, potentially hindering the achievement of optimal results. To address this gap, this study conducts an experimental comparison of various typical semantic representation methods for keywords, aiming to provide quantitative evidence for bibliometric studies. The experiment focuses on keyword clustering as the fundamental task and evaluates 22 variations of five typical methods across four scientific domains. The methods compared are co-word matrix, co-word network, word embedding, network embedding, and “semantic + structure” integration. The comparison is based on fitting the clustering results of these methods with the “evaluation standard” specific to each domain. The empirical findings demonstrate that the co-word matrix exhibits subpar performance, whereas the co-word network and word embedding techniques display satisfactory performance. Among the five network embedding algorithms, LINE and Node2Vec outperform DeepWalk, Struc2Vec, and SDNE. Remarkably, both the “pre-training and fine-tuning” model and the “semantic + structure” model yield unsatisfactory results in terms of performance. Nevertheless, even with variations in the performance of these methods, no singular approach stands out as universally superior. When selecting methods in practical applications, comprehensive consideration of factors such as corpus size and semantic cohesion of domain keywords is crucial. This study advances our understanding of semantic representation methods for keyword analysis and contributes to the advancement of bibliometric analysis by providing valuable recommendations for researchers in selecting appropriate methods.

The growing number of preprints allows reviewers to identify the authors’ identities prior to the peer review process. Yet, it remains unclear whether the preprint exposure of prestigious authors to reviewers is correlated with review features. Here, we employed the linear regression model to examine this relationship. By collecting open peer review reports of 2,059 papers published in Nature Communications in 2019 within the fields of biological and health sciences, we found no obvious difference in review features when the identities of authors with different academic prestige are potentially exposed to reviewers. Specifically, no significant effect was observed on the number of questions raised and the sentiments of the review reports (positivity and subjectivity) in the first round of the peer review process. Moreover, we found no evidence that review features from anonymous reviewers were more positively or subjectively expressed than those with reviewers’ names publicly available. The results persisted even when assuming all papers were under single-blind peer review, which were validated by using the eLife data. This study indicates that papers with both prestigious and less well-known authors are treated equally during the open peer review process, which contributes to the ongoing discourse on the fairness of peer review within the scientific community.

The Scholar's success is indicated by the number of citations it received for its publication. Examining the correlation between the linguistic attributes of scholarly publications and their scientific influence holds significant importance. This study analyzed 1000 research papers by highly ranked authors from computer science and electronics backgrounds. The title, abstract, and conclusion sections of the paper were analyzed. This study utilizes readability, lexical diversity, lexical density, syntactic features, and coherence measures to establish the correlation between citations and the textual content of an article. The characteristics of the publication were evaluated in relation to its research impact, which was classified into two categories, high citations and low citations. Additionally, the influence of various aspects on citations was assessed through the utilisation of the negative binomial regression model, ordinary least square model, and spearman correlation. This analysis took into account the characteristics of length and structure. The results highlight a clear positive link between abstract readability, and number of references with increased citations. Additionally, each additional page contributes to a 0.2 % increase in citation count. However, the number of diagrams and conclusion readability show no significant connection with citations. Factors like title length, abstract length, and conclusion length also exhibit associations, though with slightly lower percentages. The results indicate that linguistic characteristics exert a limited impact on the acquisition of citations.

Whether interdisciplinarity leads to greater success in research remains a question that is still unresolved. Investigating the impact of interdisciplinarity of scientific papers on the durability of their citation diffusion is of significant importance. Combining the concept of discontinuance in the theory of innovation diffusion and citation trajectory scenarios, this study proposes the definition and measurement indicators of Citation Discontinuance (CD), and examines the feasibility of using CD as a descriptor for the durability of citation diffusion. Using CD-related features as the dependent variable, hierarchical multiple regression is employed to explore the influence of interdisciplinarity of scientific papers on the durability of citation diffusion. The findings reveal that CD is commonly observed in citation diffusion and can serve as an indicator for describing the durability of citation diffusion. From the perspective of CD, the interdisciplinarity of scientific papers shows a positive impact on the durability of citation diffusion. This effect will also vary by discipline.

The measurement of distance between research disciplines involves various approaches, with a focus on publication citation analysis. However, calculating discipline distance requires more than just selecting relevant information; it also involves choosing suitable quantification methods and similarity measures. In this paper, we introduce a novel approach to measuring the distance between research disciplines, referred to as a distance matrix. This approach is particularly useful when there is limited availability of citation data, providing an alternative method for quantifying the distance between disciplines. Our method counts co-occurrences of disciplines based on researcher collaborations in projects and evaluates various similarity measures to convert the co-occurrence matrix into a similarity matrix. We analyze the behavior of different similarity measures and propose functions to transform the similarity matrix into a distance matrix, capturing research discipline dissimilarity effectively. Additionally, we establish evaluation criteria for distance matrix quality. We implement our approach on the Flanders Research Information Space dataset, showing promising results. The distance matrix demonstrates satisfactory density scores, outperforming traditional approaches in skewness and deviation. The probability density functions of distances remain consistent over time, indicating stability. Furthermore, the distance matrix proves valuable for visualizing discipline profiles associated with the dataset, providing valuable insights.

This study reveals that, following bilateral reduced international visitation and academic exchange, Sino-American scientific collaboration is positioned at a turning point in a declining course. American international students originating from China have declined by nearly 22 %, and American students studying in China plummeted to 1.8 % of the number in 2018–2019. US-China interdependence in scientific collaboration has also reduced remarkably. At the same time, the concentration of influential research collaborated between the United States and China is consistently greater than both nations’ research outputs. Following the discussion of possible substitutes and the roles of American and Chinese researchers in global basic science and emerging issues, I argue that the two nations are so entwined in scientific collaboration that an adversarial rivalry perspective misses much of reality. In the face of rising uncertainties and global disasters, humanity does not have time to waste on nationalistic competitions. It is time for visionary leadership from both countries to promote intellectual exchange and scientific collaboration to address pressing global challenges.

In this study, we examine the impact of government-sponsored international mobility on researchers’ scientific collaboration and productivity. To identify causal effects, we use a longitudinal dataset covering internationally mobile doctoral students sponsored by the China Scholarships Council for non-degree studies and non-mobile doctoral students while implementing a combined propensity-score matching and difference-in-differences approach. We find that international mobility has a significantly positive impact on researchers’ scientific collaboration and research output. Our findings suggest that international mobility influences individuals’ research output by increasing the size of collaboration teams. We further find that the effects of international mobility are heterogeneous, that they vary significantly across gender, prestige of doctoral institution, mobility time and destination: male researchers gain more benefits from international mobility in the numbers of collaborators and papers; mobility in early years are more beneficial in increasing collaborators; mobility to Asia and Oceania is most beneficial in improving research quality. These findings provide a deeper understanding of how international mobility shapes researchers’ academic performance and have implications for the policy formulation on government-sponsored international mobility.

Academic genealogy (AG) provides valuable insights into the transmission of knowledge from mentors to mentees, revealing the evolution of knowledge within the academic community. This study explores the intricate dynamics of knowledge evolution within academic genealogies, utilizing on a dataset comprising 16,852 computer science researchers, 613,277 papers, and 11,988 mentorship relationships. By focusing on small-scale knowledge units, our analysis aims to uncover patterns of knowledge inheritance and mutation across different subfields of computer science and highlights several aspects of knowledge evolution in computer science. Firstly, computer science is characterized by strong mentorship ties, indicating the significance of knowledge transmission within the field. Additionally, there is a mix of foundational and developing areas, suggesting a field that is growing and diversifying rather than declining, as indicated by linear regression outcomes. Secondly, our research reveals a surge in collaborative knowledge exchange in computer science since 2000, with fields such as Computer-Communication Networks and Software Engineering leading in terms of output and impact. Furthermore, areas like Computer Graphics and Artificial Intelligence stand out for their depth and novelty. Thirdly, we categorize researchers into three types: roots, branches, and leaves, reflecting their role in knowledge transmission. Branch researchers tend to innovate, while leaf researchers show a combination of traditional knowledge uptake and new contributions, illustrating the dynamic flow of ideas within the field. Future research endeavors are encouraged to embrace larger datasets and further fortify our understanding of the topic.