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This study explores whether interdisciplinary components' citation intensity (ICCI) affects papers' scientific impact. In this study, the term “interdisciplinary components” refers to the disciplines that are different from the discipline to which the target research belongs. The citation intensity is the degree of density or sparseness of the paper citation network for a discipline. Previous studies have shown that the scientific impact of interdisciplinary research is influenced by interdisciplinarity and its properties, namely, variety, balance and disparity. However, the effect of ICCI on scientific impact has not been comprehensively explored.

This study is based on the entire publication database of the Web of Science for the year 2000, where the authors provide an indicator to measure the ICCI of each publication. A tobit regression model is used to examine the effect of ICCI on scientific impact, controlling for a range of variables associated with the characteristics of the publications studied.

The results show that ICCI has a positive effect on scientific impact. The authors’ results further point out that ICCI displays a curvilinear inverted U-shape relationship with scientific impact. It means that including more citation-intensive interdisciplinary components can increase the scientific impact of interdisciplinary research. However, excessive use of citation-intensive interdisciplinary components may reduce the scientific impact of interdisciplinary research.

This study shows that, in addition to interdisciplinarity, the scientific impact of interdisciplinary research is also affected by the citation characteristics of interdisciplinary components, namely ICCI.

Research objects, such as datasets and classification standards, are difficult to be incorporated into a document-centric framework of citations, which relies on unique citable works. The Diagnostic and Statistical Manual for Mental Disorder (DSM)—a dominant classification scheme used for mental disorder diagnosis—however provides a unique lens on examining citations to a research object, given that it straddles the boundaries as a single research object with changing manifestations.

Using over 180,000 citations received by the DSM, this paper analyzes how the citation history of DSM is represented by its various versions, and how it is cited in different knowledge domains as an important boundary object.

It shows that all recent DSM versions exhibit a similar citation cascading pattern, which is characterized by a strong replacement effect between two successive versions. Moreover, the shift of the disciplinary contexts of DSM citations can be largely explained by different DSM versions as distinct epistemic objects.

Based on these results, the authors argue that all DSM versions should be treated as a series of connected but distinct citable objects. The work closes with a discussion of the ways in which the existing scholarly infrastructure can be reconfigured to acknowledge and trace a broader array of research objects.

This paper connects quantitative methods and an important sociological concept, i.e. boundary object, to offer deeper insights into the scholarly communication system. Moreover, this work also evaluates how versioning, as a significant yet overlooked attribute of information resources, influenced the citation patterns of citable objects, which will contribute to more material-oriented scientific infrastructures.

The issue of duplicate publications has received a lot of attention in the medical literature, but much less in the information science community. This paper aims to analyze the prevalence and scientific impact of duplicate publications across all fields of research between 1980 and 2007.

The approach is a bibliometric analysis of duplicate papers based on their metadata. Duplicate papers are defined as papers published in two different journals having: the exact same title; the same first author; and the same number of cited references.

In all fields combined, the prevalence of duplicates is one out of 2,000 papers, but is higher in the natural and medical sciences than in the social sciences and humanities. A very high proportion (>85 percent) of these papers are published the same year or one year apart, which suggest that most duplicate papers were submitted simultaneously. Furthermore, duplicate papers are generally published in journals with impact factors below the average of their field and obtain lower citations.

The paper provides clear evidence that the prevalence of duplicate papers is low and, more importantly, that the scientific impact of such papers is below average.

This essay explores how women scholars grapple with gender and racial inequality during a syndemic. Using a culturally comparative lens, two mother-scholars, one Afro-Boricua who identifies as Black and the other Thai who identifies as Asian, examine this topic through a comparative international womanist theoretical framework. This discussion provides a brief overview of the challenges faculty women of color have faced around the world in contemporary history. It also interrogates how the professional identities of these scholars inform their teaching, scholarship, and personal lives during a period fraught with anti-Blackness and anti-Asian hostility, gender bias, familial demands, and heightened fear and isolation. Through counter-narratives, their lived experiences are placed into a global context and insightful comparisons spotlight specific challenges that uniquely converge for women of color in the academy. This analytical discussion reflects trends in the field of comparative education by examining the impact of gender and racial discrimination on women scholars of color within political, economic, social, and cultural landscapes.

The purpose of this paper is to show that the journal impact factor (IF) is not able to reflect the full impact of scholarly journals and provides an overview of alternative and complementary methods in journal evaluation.

Aslib Proceedings (AP) is exemplarily analyzed with a set of indicators from five dimensions of journal evaluation, i.e. journal output, content, perception and usage, citations and management to accurately reflect its various strengths and weaknesses beyond the IF.

AP has become more international in terms of authors and more diverse regarding its topics. Citation impact is generally low and, with the exception of a special issue on blogs, remains world average. However, an evaluation of downloads and Mendeley readers reveals that the journal is an important source of information for professionals and students and certain topics are frequently read but not cited.

The study is limited to one journal.

An overview of various indicators and methods is provided that can be applied in the quantitative evaluation of scholarly journals (and also to articles, authors and institutions).

After a publication history of more than 60 years, this analysis takes stock of AP, highlighting strengths and weaknesses and developments over time. The case study provides an example and overview of the possibilities of multidimensional journal evaluation.

The purpose of this paper is to present the landscape of the cash-per-publication reward policy in China and reveal its trend since the late 1990s.

This study is based on the analysis of 168 university documents regarding the cash-per-publication reward policy at 100 Chinese universities.

Chinese universities offer cash rewards from USD30 to USD165,000 for papers published in journals indexed by Web of Science, and the average reward amount has been increasing for the past ten years.

The cash-per-publication reward policy in China has never been systematically studied and investigated before except for in some case studies. This is the first paper that reveals the landscape of the cash-per-publication reward policy in China.

The purpose of this paper is to analyze the disciplinary orientation of scientific publications that were mentioned on different social media platforms, focussing on their differences and similarities with citation counts.

Social media metrics and readership counts, associated with 500,216 publications and their citation data from the Web of Science database, were collected from Altmetric.com and Mendeley. Results are presented through descriptive statistical analyses together with science maps generated with VOSviewer.

The results confirm Mendeley as the most prevalent social media source with similar characteristics to citations in their distribution across fields and their density in average values per publication. The humanities, natural sciences, and engineering disciplines have a much lower presence of social media metrics. Twitter has a stronger focus on general medicine and social sciences. Other sources (blog, Facebook, Google+, and news media mentions) are more prominent in regards to multidisciplinary journals.

This paper reinforces the relevance of Mendeley as a social media source for analytical purposes from a disciplinary perspective, being particularly relevant for the social sciences (together with Twitter). Key implications for the use of social media metrics on the evaluation of research performance (e.g. the concentration of some social media metrics, such as blogs, news items, etc., around multidisciplinary journals) are identified.

The purpose of this paper is to assess high-dimensional visualisation, combined with pattern matching, as an approach to observing dynamic changes in the ways people tweet about science topics.

The high-dimensional visualisation approach was applied to three science topics to test its effectiveness for longitudinal analysis of message framing on Twitter over two disjoint periods in time. The paper uses coding frames to drive categorisation and visual analytics of tweets discussing the science topics.

The findings point to the potential of this mixed methods approach, as it allows sufficiently high sensitivity to recognise and support the analysis of non-trending as well as trending topics on Twitter.

Three topics are studied, these illustrate a range of frames, but results may not be representative of all science topics.

Funding bodies increasingly encourage scientists to participate in public engagement. As social media provides an avenue actively utilised for public communication, understanding the nature of the dialog on this medium is important for the scientific community and the public at large.

This study differs from standard approaches to the analysis of micropost data, which tend to focus on large-scale data sets. It provides evidence that this approach enables practical and effective analysis of the content of midsize to large collections of microposts.

The purpose of this paper is to analyze the tweeting behavior of 37 astrophysicists on Twitter and compares their tweeting behavior with their publication behavior and citation impact to show whether they tweet research-related topics or not.

Astrophysicists on Twitter are selected to compare their tweets with their publications from Web of Science. Different user groups are identified based on tweeting and publication frequency.

A moderate negative correlation (ρ=−0.339) is found between the number of publications and tweets per day, while retweet and citation rates do not correlate. The similarity between tweets and abstracts is very low (cos=0.081). User groups show different tweeting behavior such as retweeting and including hashtags, usernames and URLs.

The study is limited in terms of the small set of astrophysicists. Results are not necessarily representative of the entire astrophysicist community on Twitter and they most certainly do not apply to scientists in general. Future research should apply the methods to a larger set of researchers and other scientific disciplines.

To a certain extent, this study helps to understand how researchers use Twitter. The results hint at the fact that impact on Twitter can neither be equated with nor replace traditional research impact metrics. However, tweets and other so-called altmetrics might be able to reflect other impact of scientists such as public outreach and science communication.

To the best of the knowledge, this is the first in-depth study comparing researchers’ tweeting activity and behavior with scientific publication output in terms of quantity, content and impact.