SCI期刊影响因子膨胀加剧及其对科研评价的影响

使用2015—2022年Clarivate Analytics发布的期刊引证报告(JCR)数据进行统计分析,探究近年来SCI期刊的影响因子(JIF)变化特征及其对科研评价的影响。研究发现,2015—2022年期间高JIF的SCI期刊比例增加,低JIF的SCI期刊比例下降,JIF整体出现连年上涨趋势,且2021、2022年涨幅显著增加。这说明SCI期刊的JIF正在经历泡沫式膨胀且情况日益加剧,而中国SCI期刊的JIF增长率更甚于世界平均水平。JIF膨胀表面是论文数量和参考文献列表长度扩张的产物,但受益期刊精英群体和受益作者群体的推崇才是其增长的内在驱动,其侧面反映了追求高JIF的学术风气依然高涨,将造成JIF指标的进一步强化和滥用,引发更多有关结构性歧视和学术公平性的矛盾。此外,JIF前50位期刊多数保持稳定,其中知名期刊家族占有举足轻重的地位;而中国SCI期刊数量仅占世界总量的1.45%,在2022年JIF前50位期刊中只有1家,国际影响力较小。培育国内高影响力期刊,完善学术期刊评价体系,进行期刊质量、效益、贡献多维评价,并结合同行评议开展科研综合评价,是破“SCI至上”、弱化JIF膨...     

Predicting future influence of papers,researchers, and venues in a dynamic academic network

Performance evaluation and prediction of academic achievements is an essential task for scientists, research organizations, research funding bodies, and government agencies alike. Recently, heterogeneous networks have been used to evaluate or predict performance of multi-entities including papers, researchers, and venues with some success. However, only a minimum of effort has been made to predict the future influence of papers, researchers and venues. In this paper, we propose a new framework WMR-Rank for this purpose. Based on the dynamic and heterogeneous network of multiple entities, we extract seven types of relations among them. The framework supports useful features including the refined granularity of relevant entities such as authors and venues, time awareness for published papers and their citations, differentiating the contribution of multiple coauthors to the same paper, amongst others. By leveraging all seven types of relations and fusing the rich information in a mutually reinforcing style, we are able to predict future influence of papers, authors and venues more precisely. Using the ACL dataset, our experimental results demonstrate that the proposed approach considerably outperforms state-of-the art competitors.     

How research becomes impact: Librarians helping faculty use scholarly metrics to select journals

Many librarians support faculty with the publishing process, which includes journal selection and evaluating the impact of their scholarly output. While large universities have the resources for entire departments devoted to bibliometrics, the authors of this article give strategies for faculty publishing support at a smaller liberal arts university. The authors created a LibGuide with publishing resources and presented the initiative to several academic divisions. Faculty were surveyed, and the results indicated that the majority of respondents were interested in assessing journal quality and viewed the library as a resource for help with the publishing process.     

Influence of co-authorship networks in the research impact: Ego network analyses from Microsoft Academic Search

The main objective of this study is to analyze the relationship between research impact and the structural properties of co-author networks. A new bibliographic source, Microsoft Academic Search, is introduced to test its suitability for bibliometric analyses. Citation counts and 500 one-step ego networks were extracted from this engine. Results show that tiny and sparse networks – characterized by a high Betweenness centrality and a high Average path length – achieved more citations per document than dense and compact networks – described by a high Clustering coefficient and a high Average degree. According to disciplinary differences, Mathematics, Social Sciences and Economics & Business are the disciplines with more sparse and tiny networks; while Physics, Engineering and Geosciences are characterized by dense and crowded networks. This suggests that in sparse ego networks, the central author have more control on their collaborators being more selective in their recruitment and concluding that this behaviour has positive implications in the research impact.     

Double rank analysis for research assessment

Reliable methods for the assessment of research success are still in discussion. One method, which uses the likelihood of publishing very highly cited papers, has been validated in terms of Nobel prizes garnered. However, this method cannot be applied widely because it uses the fraction of publications in the upper tail of citation distribution that follows a power law, which includes a low number of publications in most countries and institutions. To achieve the same purpose without restrictions, we have developed the double rank analysis, in which publications that have a low number of citations are also included. By ranking publications by their number of citations from highest to lowest, publications from institutions or countries have two ranking numbers: one for their internal and another one for world positions; the internal ranking number can be expressed as a function of the world ranking number. In log–log double rank plots, a large number of publications fit a straight line; extrapolation allows estimating the likelihood of publishing the highest cited publication. The straight line derives from a power law behavior of the double rank that occurs because citations follow lognormal distributions with values of μ and σ that vary within narrow limits.     

Comparison of two article-level,field-independent citation metrics: Field-Weighted Citation Impact (FWCI) and Relative Citation Ratio (RCR)

We reproduce the article-level, field-independent citation metric Relative Citation Ratio (RCR) using the Scopus database, and extend it beyond the biomedical field to all subject areas. We compare the RCR to the Field-Weighted Citation Impact (FWCI), also an article-level, field-normalised metric, and present the first results of correlations, distributions and application to research university benchmarking for both metrics. Our analyses demonstrate that FWCI and RCR of articles correlate with varying strengths across different areas of research. Additionally, we observe that both metrics are comparably stable across different subject areas of research. Moreover, at the level of universities, both metrics correlate strongly.     

A comparative study of cross-domain research output and citations: Research impact cubes and binary citation frequencies

Across the various scientific domains, significant differences occur with respect to research publishing formats, frequencies and citing practices, the nature and organisation of research and the number and impact of a given domain's academic journals. Consequently, differences occur in the citations and h-indices of the researchers. This paper attempts to identify cross-domain differences using quantitative and qualitative measures. The study focuses on the relationships among citations, most-cited papers and h-indices across domains and for research group sizes. The analysis is based on the research output of approximately 10,000 researchers in Slovenia, of which we focus on 6536 researchers working in 284 research group programmes in 2008–2012.As comparative measures of cross-domain research output, we propose the research impact cube (RIC) representation and the analysis of most-cited papers, highest impact factors and citation distribution graphs (Lorenz curves). The analysis of Lotka's model resulted in the proposal of a binary citation frequencies (BCF) distribution model that describes well publishing frequencies. The results may be used as a model to measure, compare and evaluate fields of science on the global, national and research community level to streamline research policies and evaluate progress over a definite time period.     

Quantifying the degree of research collaboration: A comparative study of collaborative measures

This article reports a comparative study of five measures that quantify the degree of research collaboration, including the collaborative index, the degree of collaboration, the collaborative coefficient, the revised collaborative coefficient, and degree centrality. The empirical results showed that these measures all capture the notion of research collaboration, which is consistent with prior studies. Moreover, the results showed that degree centrality, the revised collaborative coefficient, and the degree of collaboration had the highest coefficient estimates on research productivity, the average JIF, and the average number of citations, respectively. Overall, this article suggests that the degree of collaboration and the revised collaborative coefficient are superior measures that can be applied to bibliometric studies for future researchers.     

Research software citation in the Data Citation Index: Current practices and implications for research software sharing and reuse

The aim of this study is to explore the phenomenon of research software citation and, in particular, to draw attention to the increasing importance of this form of citation in scholarly communication. This research sheds light on the current status of formal software citation that is captured by citation databases. Data for the study were gathered from more than 67,000 research software records available in public repositories indexed by Clarivate Analytics’ Data Citation Index (DCI). The metadata characteristics of the indexed records and citation data were then analyzed. Research software was rarely cited in the DCI, suggesting that the documented reuse of research software rarely occurs or is not well documented. Institutional repositories attracted few citations and had low rate of citation. It proved impossible, however, using the available data to isolate specific identifiers that can promote formal software citation. The findings presented here offer insights into research software citation that will be of interest to funding agencies, publishers, researchers, and research organizations.     

When research assessment exercises leave room for opportunistic behavior by the subjects under evaluation

This study inserts in the stream of research on the perverse effects that PBRF systems can induce in the subjects evaluated. The authors’ opinion is that more often than not, it is the doubtful scientific basis of the evaluation criteria that leave room for opportunistic behaviors. The work examines the 2004–2010 Italian national research assessment (VQR) to test the lack of possible opportunistic behavior by universities in order to limit the penalization of their performance (and funding) due to the presence of scientifically unproductive professors in faculty. In particular, institutions may have favored “gift authorship” practices. The analysis thus focuses on the output of professors who were unproductive in the VQR publication window, but became productive (“new productives”) in the following five years. A number of universities show a higher than average share of publications by new productives that are in co-authorship exclusively with colleagues from the same university. Although this might be thought to reflect opportunistic behavior by universities, the empirical evidence does not support this assumption.