Journal of Informetrics最新文献

The practice of uploading preprints of scientific manuscripts prior to journal submission has become increasingly popular. As such, it is essential to understand the impact of the preprint version of a manuscript on the peer review process to facilitate the development of open peer review practices. In the current research, we analyze a dataset comprising 1,078 biomedical papers published in Nature Communications and eLife in 2019, along with their manuscript information posted on preprint servers and their peer review histories. Our investigation focuses on the relationship between the readability of manuscript before journal submission, as represented by preprints, and the sentimental features expressed by reviewers. Based on empirical analysis utilizing a linear regression model, it has been found that reviewers are inclined to express positive sentiments towards preprints characterized by technical language, as indicated by low value on the readability indices. Additional subgroup analysis suggests that this positive effect is more pronounced in papers with lower social and scientific impact, as indicated by online attention scores and scholarly views after publication, respectively. Overall, results of our analysis reveals that the utilization of technical language characterized by lower readability level in academic papers does not seem to hinder the peer review process in biomedical science, which has significant implications for the open peer review practice.

With the formation of the fourth paradigm of scientific research, algorithms have become increasingly important in scientific research. In academic papers, algorithms may be mentioned by scholars with various motivations, using, comparing, or improving algorithms to solve complex research tasks. Identifying these motivations can help scholars discover the relationships between algorithms and further assess their roles and values. Therefore, taking the field of natural language processing (NLP) as an example, this article proposes a complete method to conduct the identification, distribution, and evolution of motivations for mentioning algorithms at the sentence level. Specifically, using manual annotation and machine learning methods, we identify algorithm entities and sentences in the full text of papers, classify motivations for mentioning algorithms by pre-training models and data augmentation techniques, and finally analyze the distribution and evolution of motivations. The results show that the deep learning models trained with the augmented data outperform the traditional machine learning models in the classification task. In academic papers, more than half of the sentences show the direct use of algorithms, while the lowest percentage of motivations are improving algorithms, and the diversity of motivations has been increasing with time. For specific algorithms, grammatical algorithms are mentioned more by the motivation of “description,” while more motivations of “use” are found in the machine learning algorithms category. As time passed, the “use” motivations gradually replaced the “description” motivations for different algorithms, and the number of motivation types decreased significantly. Our research explores the identification, distribution, and evolution of authors’ motivations for mentioning algorithm entities, which could provide a basis for future algorithm relationship identification and influence evaluation using motivations.

Existing studies on the detection of emerging scientific topics (ESTs) overemphasize the newness and neglect content innovation of knowledge. Moreover, they also ignore the lag existing in knowledge diffusion. In this paper, we propose a four-stage detection framework for ESTs that maps emerging attributes from paper entities to scientific topics. Empirical studies based on two significantly different disciplinary datasets, IS-LS, and AI, which contain 73,601 and 255,620 publications, respectively, are employed to validate our approach. First, we generate 29 and 47 candidate scientific topics based on topic modeling, respectively. Second, we represent the novelty of paper entities based on pre-trained language models, which is mapped to scientific topic entities along with knowledge distributions to obtain topic emerging attributes: topic novelty, relative share and growth. Third, we propose to predict future trends of these attributes with Neural Prophet, which outperforms four baseline models in $R^2$, $MAE$ and $RMSE$. Finally, combining future values of candidate scientific topics, they are grouped into 8 clusters containing two ESTs types through strategic market theory and clustering model. From the correlation and feature distribution analysis of emerging attributes, we discover the existence of resilience and scale advantage in the diffusion of scientific knowledge. There also exists significant uncertainty in previous citation-based scientific topic evaluation patterns caused by the complexity of citation behavior. Overall, this research enriches theoretical knowledge and detection frameworks of ESTs, and provides detailed insights into comprehensive assessment and dissemination of scientific topics.

While advantages of global homogenization and standardization have been discussed on scientific and technological research activities, specific discussion on the disadvantages to generate scientific innovation has been limited. In this study, we aim to clarify the impact of globalization to generate emerging topics in life science and medicine by applying the emerging keywords (EKs) and highly successful emerging keywords (HS-EKs) methodology, which represent scientometric elements of emerging topics and high-impact emerging topics, respectively, as indicators. We analyzed all paper output from 53 countries using PubMed and found a global increase in paper output and EK generation in line with economic growth in the past 50 years. However, the efficiency to generate scientific innovation, reflected in HS-EKs, was significantly reduced and this effect was independent of country-level economic output. We also reported homogenization in research topics over the study period as a possible factor for the observed decrease in HS-EK generation efficiency. Finally, we discuss the foundational issues that gave rise to homogenized science, the impact on scientific innovation, and what policies might be necessary to repair the scientific innovation-generating engine.

The document 'Foundations of Narrative Bibliometrics' provides an analysis of the evolution of scientific assessment, highlighting the influence of manifestos such as DORA and CoARA in shaping ethical and responsible practices in academia, as well as their assimilation by Spanish scientific policies. It connects this context with the contributions of evaluative bibliometrics, emphasising the transition towards a more integrative approach that advocates for a balance between quantitative and qualitative methods in research evaluation. Furthermore, it underscores how the Narrative Curriculum has emerged as one of the fundamental tools in new evaluation processes, as it allows for the description of the complexity and context of academic achievements. Narrative Bibliometrics is proposed, defined as the use of bibliometric indicators to generate stories that support the defence and exposition of a scientific curriculum and/or its individual contributions within the framework of a scientific evaluation process, which demands narratives. To introduce the reader, it presents, in a non-exhaustive manner, sources, indicators, and practical cases for effectively applying Narrative Bibliometrics in various scientific evaluation contexts. Hence, this document contributes to the responsible use of bibliometric indicators, serving as a tool for evaluators and researchers.

The highly skewed nature of research influence has been widely acknowledged. Among extant studies examining contributing factors, most focus on the hard sciences in developed economies with very few examining the social sciences in emerging powers. The impact of citation scope is likewise left largely underexplored. In this paper, we develop two novel measures of citation scope using geography and research field as metrics and explore their role in boosting academic impact. Our results support geography as a citation scope serving an important pathway through which international collaboration affects academic impact. Such effect increases in prominence in later years. We do not find evidence indicating the mediating effect of research field citation scope on scholarly recognition.

Methods to rank documents in large-scale citation data are increasingly assessed in terms of their ability to identify small sets of expert-selected papers. Here, we propose an algorithm for the accurate identification of milestone papers from citation networks. The algorithm combines an influence propagation process with game theory concepts. It outperforms state-of-the-art metrics in the identification of milestone papers in aggregate citation network data, while potentially mitigating the ranking's temporal bias compared with metrics that have similar milestone identification performance. The proposed method sheds light on the interplay between ranking accuracy and temporal bias.

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.