Scientometrics最新文献

This study comprehensively analyses gender representation and citation disparities in gender studies by examining the position of female scholars as first and corresponding authors. The research uncovers a pattern of gender-homogeneous co-authorship and investigates the geographical and economic disparities in academic contributions, scrutinising the impact of a country’s economic status on citation rates and open-access publications, particularly in relation to citation rates and open-access publications. The study uses a Logistics Regression and Zero-Inflated Negative Binomial Regression model to explore factors influencing open-access publication and citation rates. The study’s findings demonstrate the predominant presence of female scholars in gender-focused literature within social sciences, in contrast to their underrepresentation in STEM fields. The findings also reveal a tendency towards gender-homogenous collaborations and a significant concentration of scholarly output from the high-income regions, highlighting both geographic and economic disparities. The present study provides an analytical foundation for future studies on the global distribution of scholarly contributions and the complex interplay of various factors affecting academic publishing in gender studies.

The number of positive words in scientific papers has exhibited a notable upwards trend over the past few decades. However, there remains a gap in our comprehensive understanding of the relationship between positive words and research impact. In this study, we conduct a multifaceted exploration of the citation advantage associated with positive words based on social cognitive theory, examining its predictability, temporal evolution, and universality across journals of varying quality grades. Drawing from a corpus encompassing 124,144 papers published in the management field between 2001 and 2020, our regression results provide compelling evidence suggesting that positive words can serve as a significant predictor of the citation counts of academic papers, supporting the citation advantage of positive words. However, it is essential to recognize that over time, the citation advantage attributed to positive words is experiencing a conspicuous decline. The universality of the above phenomenon has been further verified in the analysis of journals of different quality. Our findings prompt a discussion regarding the need to pay more attention to the overuse and misuse of positive words, as well as practical considerations for enhancing scientific communication within the academic community.

A recent study published in Scientometrics used publications in Scopus and Web of Science Core Collection to exam the decades-long scientific collaboration between Cuba and China (Ronda-Pupo, Scientometrics 129:785–802, 2024). Ronda-Pupo’s finding of the significant growth of research collaboration between these two countries evidenced by the number of co-authored papers is different from our daily perception of the scientific collaboration between China and Cuba. By using the same data, we find the dominating role of multilateral co-authorship rather than bilateral or trilateral co-authorship in Cuba-China scientific collaboration. This important finding gives an alternative explanation of the increasing Cuba-China co-authored publications. Through the supplement of our exploration, readers can have a better understanding of the Cuba-China scientific collaboration.

Exploring a robust and universal appeal bibliometric indicator for assessing creativity is essential but challenging. The novelty measure of innovation proposed by Uzzi et al. (NoveltyU) has sparked considerable interest and debate. Thus, further validation and understanding of its portfolio form of novelty and scope of application are necessary. This paper delves into the calculation and application of the NoveltyU method to shed light on its effectiveness and scope. Analysis of the calculation process reveals that journal pairs with higher novelty often span independent fundamental areas, while those with lower novelty tend to focus on similar and applied fields. Utilizing collaboration patterns between institutions, as discussed in our prior study (Fan et al., Scientometrics 125:1179–1196, 2020), offers insight into the method’s performance in real-world contexts. Results consistently show higher mean NoveltyU values in MM pattern over time, affirming the method’s validity. Categorizing papers into high conventional, low conventional, low novel, and high novel categories unveils higher overlap degree of terms among different patterns in high novel papers. Moreover, leading terms in MM pattern exhibit specific information, while delay terms tend to be more general, and simultaneous terms are even more so. These findings offer valuable insights into identifying hot and frontier topics, bolstering the credibility and application potential of the NoveltyU method, aligning with the broader objective of establishing valid measures of innovativeness in research.

Many claims have been made in the past that Management and Organization Studies (MOS) is becoming increasingly fragmented, and that this fragmentation is causing it to drift into self-reference and irrelevance. Despite the weight of this claim, it has not yet been subjected to a systematic empirical test. This paper addresses this research gap using the tribalization approach and diachronic co-citation analyses. Based on 22,430 papers published in 14 MOS journals between 1980 and 2019, we calculate local and global centrality measures and the flow of cited articles between co-citation communities over time. In addition, we use a node-removal strategy to test whether only ritualized citations ensure MOS cohesion. Rather than tribalization, our results suggest a center–periphery structure. Furthermore, more peripheral papers are integrated into the central co-citation communities, but the lion's share of the flow of cited papers occurs over time to only a small number of large clusters. An increase of fragmentation and crowding-out of smaller clusters in MOS in seen in the polycentrically organized core 2014–2019.

As the volume of scientific literature expands rapidly, accurately gauging and predicting the citation impact of academic papers has become increasingly imperative. Citation counts serve as a widely adopted metric for this purpose. While numerous researchers have explored techniques for projecting papers’ citation counts, a prevalent constraint lies in the utilization of a singular model across all papers within a dataset. This universal approach, suitable for small, homogeneous collections, proves less effective for large, heterogeneous collections spanning various research domains, thereby curtailing the practical utility of these methodologies. In this study, we propose a pioneering methodology that deploys multiple models tailored to distinct research domains and integrates early citation data. Our approach encompasses instance-based learning techniques to categorize papers into different research domains and distinct prediction models trained on early citation counts for papers within each domain. We assessed our methodology using two extensive datasets sourced from DBLP and arXiv. Our experimental findings affirm that the proposed classification methodology is both precise and efficient in classifying papers into research domains. Furthermore, the proposed prediction methodology, harnessing multiple domain-specific models and early citations, surpasses four state-of-the-art baseline methods in most instances, substantially enhancing the accuracy of citation impact predictions for diverse collections of academic papers.

Metaphors play a crucial role in facilitating the comprehension and analysis of knowledge. “Knowledge as energy” is a well-established metaphorical framework that provides unique benefits for comprehending the dissemination of knowledge and enabling its quantification. Nevertheless, empirical studies employing this framework are limited, especially in the area of the work–energy metaphor, which primarily remains theoretical. This paper proposes an application scheme for the work– energy metaphor in interdisciplinary citation analysis. In this scheme, disciplines are considered entities; various factors that drive the progress of a discipline are considered forces; energy is considered the knowledge produced or transferred in the citations. Building upon the work–energy theorem in physics, this study developed indicators reflecting citation quality and velocity to assess interdisciplinary research progression. An empirical investigation was carried out, utilizing these indicators to evaluate the influence of interdisciplinary citations on disciplines. In the experiments, we used Library and Information Science (LIS) from 2012 to 2021 as an example to analyze the impact of interdisciplinary citations from LIS on other disciplines over two time periods. The experiments demonstrated the feasibility of the work–energy metaphorical framework proposed in this paper. It was also found that Computer Science, Management, and Business experienced the highest impact from LIS interdisciplinary citations and exhibited steady growth over a 10-year period. Environmental Science has substantial potential for the future.

This investigation explores the impact of geographical names within article titles on citation frequency across a corpus of literature within the field of library and information science, spanning from 2018 to 2020, and encompassing 56 journal titles. We hypothesized that the presence of geographical names of nations in article titles would negatively correlate with citation counts. Our primary analysis of 1330 articles with geographical names in titles versus 8702 without, revealed a statistically significant, albeit small, difference in median citations, favoring articles without geographical names (mdn = 7) over those with geographical names (mdn = 6). Contrary to our secondary hypothesis, a proximity analysis demonstrated a weak, positive correlation between the position of geographical names near the title end and citation counts. Our examination found little evidence supporting differential citation frequency based on the Human Development Index (HDI) of the nations mentioned in titles. However, although a journal’s impact score strongly predicted citation counts for articles, we found that these counts were depressed when articles in those journals contained a geographic name. We found a negative correlation between the frequency of geographical names in article titles and the journals’ impact scores, yet this was weakly, statistically significant. Our data also suggested a vague positional preference for nations within titles, unrelated to HDI. Furthermore, the likelihood of journals publishing articles mentioning nations of varying HDI was found to be statistically insignificant. This study sheds light on the nuanced influence of title specificity, through geographical names, on scholarly communication and citation impact, indicating a slight preference for broader title phrasing in garnering citations.

Literature retrieval helps scientists find previous work that is relative to their own research or even get new research ideas. However, the discrepancy between retrieval results and the ultimate intention of citation is neglected by most literature retrieval models. Citation intent refers to the researcher’s motivation for citing a paper. A citation intent graph with homogeneous nodes and heterogeneous hyperedges can represent different types of citation intents. By leveraging the citation intent information included in a hypergraph, a retrieval model can guide researchers on where to cite its retrieval result by understanding the citation behaviour in the graph. We present a ranking model called CitenGL (Citation Intent Graph Learning) that aims to extract citation intent information and textual matching signals. The proposed model consists of a heterogeneous hypergraph encoder and a lightweight deep fusion unit for efficiency trade-offs. Compared to traditional literature retrieval, our model fills the gap between retrieval results and citation intention and yields an understandable graph-structured output. We evaluated our model on publicly available full-text paper datasets. Experimental results show that CitenGL outperforms most existing neural ranking models that only consider textual information, which illustrates the effectiveness of integrating citation intent information with textual information. Further ablation analyses show how citation intent information complements text-matching signals and citation networks.

This study critically evaluates gender assignment methods within academic contexts, employing a comparative analysis of diverse techniques, including a SVM classifier, gender-guesser, genderize.io, and a Cultural Consensus Theory based classifier. Emphasizing the significance of transparency, data sources, and methodological considerations, the research introduces nomquamgender, a cultural consensus-based method, and applies it to Teseo, a Spanish dissertation database. The results reveal a substantial reduction in the number of individuals with unknown gender compared to traditional methods relying on INE data. The nuanced differences in gender distribution underscore the importance of methodological choices in gender studies, urging for transparent, comprehensive, and freely accessible methods to enhance the accuracy and reliability of gender assignment in academic research. After reevaluating the problem of gender imbalances in the doctoral system we can conclude that it’s still evident although the trend is clearly set for its reduction. Finaly, specific problems related to some disciplines, including STEM fields and seniority roles are found to be worth of attention in the near future.