Journal of Informetrics最新文献

The growth of excellent scholars provides paradigmatic career paths leading to research success, as their research capabilities ultimately manifest as fluctuations in bibliometric indexes. Examining the commonalities in the trajectories of these bibliometric indexes displays the universal characteristics of their growth process, and furtherly shows exemplary routes to scientific success. In this study, we examine 287 excellent scholars elected as ACM Fellows in the field of computer science from 2016s to 2020s. Based on their changes in productivity, impact, and comprehensive abilities, we categorize them into three categories, four categories, and six categories, respectively. Most of these scholars experience continuous growth in productivity during the early development stages, maintaining a prolonged period of high productivity in the mid-later maturity stages. Their impact rises smoothly and consistently, while the growth of their comprehensive abilities is relatively gradual, remaining at above-average levels in the mid-later maturity stages. Furthermore, the level of recognition within the scientific research community varies for different categories of scholars, and there are also differences in the growth patterns between scholars from Asia and those from Western regions.

The diffusion of citation knowledge is an important measure of transdisciplinary scientific impact and the diversity of transdisciplinary citation content (sentences). Moreover, combining citation sentiment (CS) and citation aspect (CA) can help researchers identify the attitudes, ideas, or positions reflected in the evolution of scientific elements (e.g., theories, techniques, and methods). This is because of their use by scholars from different disciplines, paving the way toward transdisciplinary penetration and the development of domain knowledge through the proliferation of cited knowledge. However, most studies mainly address citation aspect classification (CAC) and citation sentiment classification (CSC) separately, ignoring their shared features of interactions. In this study, we construct a dataset for transdisciplinary citation content analysis using citations and academic full texts from the Chinese Social Sciences Citation Index (CSSCI), which includes 14,832 manually-annotated citations. Thereafter, we utilized the developed dataset to conduct a transdisciplinary fine-grained citation content analysis by combining CAC and CSC. The objective of the CAC task was to classify transdisciplinary citations into theoretical concepts (TC), methodological techniques (MT), and data information (DI), whereas the CSC task classified citations into positive, negative, and neutral classes. Furthermore, we leveraged a multi-task learning (MTL) model to perform CAC and CSC jointly and then compared its performance to those of several widely-used deep learning models. Our model achieved 83.10 % accuracy for CAC and 80.46 % accuracy for CSC, demonstrating its superiority to single-task systems. This indicates the strong correlation between the CAC and CSC of transdisciplinary citation tasks, benefiting from each other when learned concurrently. This new method can be used as an auxiliary decision support system to extend the analysis of transdisciplinary citation content.

Nonlinear field normalization citation counts at the paper level obtained using nonlinear field normalization methods should not be added or averaged. Unfortunately, there are many cases adding or averaging the nonlinear normalized citation counts of individual papers that can be found in the academic literature, indicating that nonlinear field normalization methods have long been misused in academia. In this paper, we performed the following three research works. First, we analyzed why the nonlinear normalized citation counts of individual papers should not be added or averaged from the perspective of theoretical analysis in mathematics: we provided mathematical proofs for the crucial steps of the analysis. Second, we systematically classified the existing main field normalization methods into linear and nonlinear field normalization methods. Third, we used real citation data to explore the error effects caused by adding or averaging the nonlinear normalized citation counts on practical research evaluation results. The above three research works provide a theoretical basis for the proper use of field normalization methods in the future. Furthermore, because our mathematical proof is applicable to all nonlinear data in the entire real number domain, our research works are also meaningful for the whole field of data and information science.

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.