Scientometrics最新文献

We introduce and define three types of small worlds: small worlds based on the diameter of the network (SWD), those based on the average geodesic distance between nodes (SWA), and those based on the median geodesic distance (SWMd). These types of networks are defined as limiting properties of sequences of sets. We show the exact relation between these three types, namely that each SWD network is also an SWA network and that each SWA network is also an SWMd network. Yet, having the small-world property is a phenomenon that can easily occur in the sense that most networks are small-world networks in one of the three ways. We introduce sequences of distance frequencies, so-called alpha-sequences, and prove a relation between the majorization property between alpha-sequences and small-world properties.

Standard citation-based bibliometric tools have severe limitations when they are applied to periods in the history of science and the humanities before the advent of now-current citation practices. This paper presents an alternative method involving the extracting and analysis of mentions to map and analyze links between scholars and texts in periods that fall outside the scope of citation-based studies. Focusing on one specific discipline in one particular period and language area—Anglophone philosophy between 1890 and 1979—we describe a procedure to create a mention index by identifying, extracting, and disambiguating mentions in academic publications. Our mention index includes 1,095,765 mention links, extracted from 22,977 articles published in 12 journals. We successfully link 93% of these mentions to specific philosophers, with an estimated precision of 82% to 91%. Moreover, we integrate the mention index into a database named EDHIPHY, which includes data and metadata from multiple sources and enables multidimensional mention analyses. In the final part of the paper, we present four case studies conducted by domain experts, demonstrating the use and the potential of both EDHIPHY and mention analyses more generally.

This study presents the result of a cross-disciplinary and diachronic examination of cohesive devices used in high citation research article (HCRA) titles, a hitherto less-explored subgenre of academic discourse. Based on Halliday and Matthiessen’s (2014) Cohesion Model, the research analyzed the employment of connectors in a self-constructed corpus of 30,000 HCRA titles from disciplines of Biology, Chemistry, Linguistics, and Music from 1980 to 2023. Comparisons of disciplinary and diachronic changes of connectors were made in two-way multivariate analyses of variance (MANOVA), and follow-up analyses of variance (ANOVA). Major findings indicate that discipline, as compared to period, is the determinant of cohesion in HCRA titles, albeit in medium effect size. The use of Extension and Enhancement prevail HCRA titles, suggesting an exponential increase of sophistication and comprehensiveness of information in the curation and dissemination of scientific knowledge. Specifically, cohesion of HCRA titles is predominantly realized by additive, temporal, and causal connectors with sharp contrasts between soft and hard sciences, indicating longer titles with these connectors attract readers by harnessing their familiarity of disciplinary knowledge. Quantitative characterization of cohesion in HCRA titles shed light on how expert writers coherently organize titles to maximize informativeness and research impact, thereby contributing pedagogically to academic writing for English for Academic and Specific Purposes, and empirically for the research on the predictability of citation impacts.

Review mills sum up a new category of reviewer misconduct that flies in the face of reviewer ethics and integrity. A pattern of generic, vague, and repeated affirmations (identical or very similar boilerplate phrasing) is noted in the analysis of 263 review reports, regardless of the scientific content of the papers under review, coupled with coercive citation (perhaps among the main reasons for such behavior), which when combined produce fake reviews. The misconduct associated with review mills is unlike mere plagiarism (self-plagiarism) of reviewer comments. It is important to quantify the problem and to take urgent measures: (a) to identify the review millers; (b) to rectify the published literature; and (c) to determine procedures for journals and publishers on procedures to counter this new type of misconduct.

Research on innovative content within academic articles plays a vital role in exploring the frontiers of scientific and technological innovation while facilitating the integration of scientific and technological evaluation into academic discourse. To efficiently gather the latest innovative concepts, it is essential to accurately recognize innovative sentences within academic articles. Although several supervised methods for classifying article sentences exist, such as citation function sentences, future work sentences, and formal citation sentences, most of these methods rely on manual annotations or rule-based matching to construct datasets, often neglecting an in-depth exploration of model performance enhancement. To address the limitations of existing research in this domain, this study introduces a semi-automatic annotation method for innovative sentences (IS) with the assistance of expert comments information and proposes a data augmentation method by SAO reconstruction to augment the training dataset. Within this paper, we compared and analyzed the effectiveness of multiple algorithms for recognizing IS within academic articles. This study utilized the full text of academic articles as the research subject and employed the semi-automatic method to annotate IS for creating the training dataset. Then, this study validated the effectiveness of the semi-automatic annotation method through manual inspection and compared it with rule-based annotation methods. Additionally, the impacts of different augmentation ratios on model performance were also explored. The empirical results reveal the following: (1) The semi-automatic annotation method proposed in this study achieves an accuracy rate of 0.87239, ensuring the validity of annotated data while reducing the manual annotation cost. (2) The SAO reconstruction for data augmentation method significantly improved the accuracy of machine learning and deep learning algorithms in the recognition of IS. (3) When the augmentation ratio in the training set was set to 50%, the trained GPT-2 model was superior to other algorithms, achieving an ACC of 0.97883 in the test set and an F1 score of 0.95505 in practical application.

Knowledge has become a crucial and foundational resource for the development of the digital economy. Employing a fixed-effects panel model and drawing upon panel data from 279 Chinese cities from 2014 to 2019, this study empirically investigates the differential impacts of two distinct knowledge recombination activities—recombinant reuse and recombinant creation—on the development of the digital economy at the city level. Additionally, the moderating role of knowledge diversification in this relationship is explored. Our findings reveal that recombinant reuse exerts a negative influence on urban digital economy development, whereas recombinant creation demonstrates a positive influence. Furthermore, this study observe that knowledge diversification plays a positive moderating role in the relationship between the two divergent types of knowledge recombination and urban digital economy development. The finding suggests that a higher degree of knowledge diversification may exacerbate the detrimental impact of recombinant reuse on urban digital economy development in cities where such activities are prevalent. Conversely, cities that prioritize recombinant creation may accrue additional benefits for digital economy growth by fostering a diverse knowledge base. This study emphasizes the significance of knowledge recombination types and knowledge structure features in digital economy development. It contributes to the enrichment of theoretical studies related to the digital economy and provides insights for policymakers in cities to formulate appropriate digital economy development strategies based on local knowledge production mechanisms.

We examine the evolution of order of authorship based on seniority during 1975–2021. Results show that for small teams (≤ 5 authors), the likelihood of placing the most junior author first has been increasing since the nineties. Additionally, the likelihood of placing the most senior author in last place has also been increasing. The results are at least partially driven by digitization of bibliographic records that drastically facilitated assignment of citations to all authors. We interpret our findings as a growing trend of small author teams becoming fairer. We do not find any significant effects for larger teams suggesting different practices when team size increases. Given that team size is, slowly but steadily, increasing over the last decades, the debate over the ethical considerations around authorship practices should place significance on the number of co-authors.

The rise of globalization and the advent of Internet gave birth to a new science model in which national systems compete for a place in a global communication network where their products could circulate and gain notoriety. Several studies have been carried out to assess national performance in such network, particularly in terms of scientific research output and collaboration networks. However, academic journals in specific disciplines have not received the same attention. The purpose of this paper was to evaluate the evolution of journal prestige in terms of country and region of origin in the field of environmental engineering in SCImago Journal and Rank database during 1999–2022. It was found that Western countries and private publishers still dominate the discipline in 2022. The United Kingdom, the United States, and the Netherlands housed 51.16% of journals in 2022. Also, corporate publishers with headquarters in these countries own most of the journals, particularly in the top tier. Elsevier, Springer, and Taylor & Francis had a total 54 journals indexed in 2022, and 65.9% of journals rank in the first quartile belonged to these groups. However, Poland, China, and Iran have become major players. By 2022, they had 12, 10, and 7 environmental engineering journals indexed in SCImago Journal and Country Rank, and China and Iran’s journals have been ranked as Q1.

With tremendous growth in the volume of published scholarly work, it becomes quite difficult for researchers to find appropriate documents relevant to their research topic. Many research paper recommendation approaches have been proposed and implemented which include collaborative filtering, content-based, metadata, link-based and multi-level citation network. In this research, a novel Research paper Recommendation system is proposed by integrating Multiple Features (RRMF). RRMF constructs a multi-level citation network and collaboration network of authors for feature integration. The structure and semantic based relationships are identified from the citation network whereas key authors are extracted from collaboration network for the study. For experimentation and analysis, AMiner v12 DBLP-Citation Network is used that covers 4,894,081 academic papers and 45,564,149 citation relationships. The information retrieval metrices including Mean Average Precision, Mean Reciprocal Rank and Normalized Discounted Cumulative Gain are used for evaluating the performance of proposed system. The research results of proposed approach RRMF are compared with baseline Multilevel Simultaneous Citation Network (MSCN) and Google Scholar. Consequently, comparison of RRMF showed 87% better recommendations than the traditional MSCN and Google Scholar.

This paper investigates the growing evidence of research-related misconduct by developing and testing a theoretical framework. We study the deep causes of misconduct by asking whether the perception of an erosion of the core academic values, formally an ideology-based psychological contract breach, is associated with research-related misconduct. We test our framework by examining the use of Sci-Hub and providing empirical evidence that the loss of faith in scientific research sparkles research-related misconduct against publishers. Based on a stratified sample of 2849 academics working in 30 institutions in 6 European countries, we find that ideology-based psychological contract breach explains Sci-Hub usage, also when controlling for other possible motivations. The magnitude of the effect depends on contextual and demographic characteristics. Females, foreign, and tenured scholars are less likely to download papers illegally when experiencing a contract breach of academic values. Our results suggest that policies restoring academic values might also address research-related misconduct.