• Digital Humanities and Cultural Heritage
• Publicationsclose
• Research dataclose
• Other research productsclose
• Article close
• 050905 science studies close
• Scientometrics close
• OpenAIRE close

The Yule-Simpson paradox refers to the fact that outcomes of comparisons between groups are reversed when groups are combined. Using Essential Sciences Indicators, a part of InCites (Clarivate), data for countries, it is shown that although the Yule-Simpson phenomenon in citation analysis and research evaluation is not common, it isn't extremely rare either. The Yule-Simpson paradox is a phenomenon one should be aware of, otherwise one may encounter unforeseen surprises in scientometric studies. ispartof: SCIENTOMETRICS vol:126 issue:4 pages:3501-3511 ispartof: location:Switzerland status: published

Scholars all over the world have produced a large body of COVID-19 literature in an exceptionally short period after the outbreak of this rapidly-spreading virus. An analysis of the literature accumulated in the first 150 days hints that the rapid knowledge accumulation in its early-stage development was expedited through a wide variety of journal platforms, a sense and pressure of national urgency, and inspiration from journal editorials.

The humanities are often characterized by sociologists as having a low mutual dependence among scholars and high task uncertainty. According to Fuchs' theory of scientific change, this leads over time to intellectual and social fragmentation, as new scholarship accumulates in the absence of shared unifying theories. We consider here a set of specialisms in the discipline of history and measure the connectivity properties of their bibliographic coupling networks over time, in order to assess whether fragmentation is indeed occurring. We construct networks using both reference overlap and textual similarity. It is shown that the connectivity of reference overlap networks is gradually and steadily declining over time, whilst that of textual similarity networks is stable. Author bibliographic coupling networks also show signs of a decline in connectivity, in the absence of an increasing propensity for collaborations. We speculate that, despite the gradual weakening of ties among historians as mapped by references, new scholarship might be continually integrated through shared vocabularies and narratives. This would support our belief that citations are but one kind of bibliometric data to consider --- perhaps even of secondary importance --- when studying the humanities, while text should play a more prominent role.

Understanding semantic word shifts in scientific domains is essential for facilitating interdisciplinary communication. Using a data set of published papers in the field of information retrieval (IR), this paper studies the semantic shifts of words in IR based on mining per-word topic distribution over time. We propose that semantic word shifts not only occur over time, but also over topics. The shifts are examined from two perspectives, the topic-level and the context-level. According to the over-time word-topic distribution, stable words and unstable words are recognized. The diverging and converging trends in the unstable type reveal characteristics of the topic evolution process. The context-level shifts are further detected by similarities between word vectors. Our work associates semantic word shifts with the evolving of topics, which facilitates a better understanding of semantic word shifts from both topics and contexts.

This letter describes the author’s reactions to being cited 10,000 times in Google Scholar. Data are provided to illustrate differences between the numbers of citations for books and edited works and between papers on different topics and in different styles.

The decision of reading or not a research paper is commonly made while reading its title and abstract. Although content and merit should lead to that decision, other factors such as writing style may intervene. Eventually, more readings could produce more citations. We investigated the stylistic factors in the title and abstract of research papers that affect their “citability”, and built a prediction model for citations at 5, 10, and 15 years. Since the number of citations is the preferred ranking function of several academic search engines, our “citability” function could alleviate the under-representation of recent not-yet-cited papers in query results. For this study, we collected a large dataset of around 750,000 titles and abstracts from articles in Scopus, intended to be representative of the entire science. For each instance, we extracted a relatively large set of 3578 stylistic features that were extracted at different linguistic levels, i.e. characters, syllables, tokens (i.e. words), sentences, stop/content words, and part-of-speech (POS) tags. Particularly, we present a novel set of corpus-based stylistic features that we called Corpus Spectral Signatures (CSS). We found out that a linear prediction model for citations (binned into quartiles) build with only the top-250 correlated features achieved a mean absolute error of 0.805 quartiles, and that on average, predictions were highly correlated with their real values (Spearman’s $$rho=0.515$$ ). CSS features were among the top correlated features, but POS features were the most predictive group of features in an ablation study.

Can famous economics scholars extend their prominence to the time after their deaths? This question is analyzed for the period 1925–2018 for Nobel Prize laureates. We find that Nobel Prize winners who die prematurely are more likely to experience a marked reduction of attention from their peers, as measured by citations. In contrast, death does not produce this effect for famous economists dying at old age. A few scholars who died prematurely are an exception to the downward trend in attention after death. Such exceptions include Clive Granger, Elinor Ostrom, and to some extent Leonid Kantorovich.

Citation impact indicators play a significant role in evaluating the scientific research activity. Most of citation impact indicators are based on the citation count that the publication is cited as a reference in the other publications, but the difference between each citation situation was not considered. Normally, the number of citations that a publication is cited in the other publications may represent the formal quality of the publication. Similarly, the number of times that a publication is really mentioned within the citing publication, it may also represent the formal quality of the citation. We have examined about how many times each reference was really mentioned within the citing publications and studied about the citation situation within the citing publications. We verified that the citation distribution of references according to the mention frequency follows the Generalized Pareto distribution. The results showed that about 20% of total references were mentioned three and more times, and the number of citation mentions for the about 50% of total references were from about 20% of the total references in the citing publications.

This article describes a procedure to generate a snapshot of the structure of a specific scientific community and their outputs based on the information available in Google Scholar Citations (GSC). We call this method MADAP (Multifaceted Analysis of Disciplines through Academic Profiles). The international community of researchers working in Bibliometrics, Scientometrics, Informetrics, Webometrics, and Altmetrics was selected as a case study. The records of the top 1,000 most cited documents by these authors according to GSC were manually processed to fill any missing information and deduplicate fields like the journal titles and book publishers. The results suggest that it is feasible to use GSC and the MADAP method to produce an accurate depiction of the community of researchers working in Bibliometrics (both specialists and occasional researchers) and their publication habits (main publication venues such as journals and book publishers). Additionally, the wide document coverage of Google Scholar (specially books and book chapters) enables more comprehensive analyses of the documents published in a specific discipline than were previously possible with other citation indexes, finally shedding light on what until now had been a blind spot in most citation analyses. This is a post-peer-review, pre-copyedit version of an article published in Scientometrics. The final authenticated version is available online at: https://doi.org/10.1007/s11192-017-2587-4 Research funded by Ministerio de Educación, Cultura y Deporte (FPU2013/05863). Universitat Politècnica de València (PAID-10-14).

Intriguing unforced regularities in human behaviors have been reported in varied research domains, including scientometrics. In this study we examine the manuscript submission behavior of researchers, with a focus on its monthly pattern. With a large and reliable dataset which records the submission history of articles published on 10 multidisciplinary journals and 10 management journals over a five-year period (2013-2017), we observe a prominent turn-of-the-month submission effect for accepted papers in management journals but not multidisciplinary journals. This effect gets more pronounced in submissions to top-tier journals and when the first day of a month happens to be a Saturday or Sunday. Sense of ceremony is proposed as a likely explanation of this effect, since the first day of a month is a fundamental temporal landmark which has a 'fresh start effect' on researchers. To conclude, an original and interesting day-of-the-month effect in the academia is reported in this study, which calls for more research attention.