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Abstract

Purpose

The number of citations has been widely used to measure the significance of a paper. However, there is a need in introducing another index to determine superiority or inferiority of papers with the same number of citations. We determine superiority or inferiority of papers by using the ranking based on the number of citations and PageRank.

Design/methodology/approach

We show the positive linear correlation between Citation Rank (the ranking of the number of citation) and PageRank. On this basis, we identify high-quality, prestige, emerging, and popular papers.

Findings

We found that the high-quality papers belong to the subjects of biochemistry and molecular biology, chemistry, and multidisciplinary sciences. The prestige papers correspond to the subjects of computer science, engineering, and information science. The emerging papers are related to biochemistry and molecular biology, as well as those published in the journal “Cell.” The popular papers belong to the subject of multidisciplinary sciences.

Research limitations

We analyze the Science Citation Index Expanded (SCIE) from 1981 to 2015 to calculate Citation Rank and PageRank within a citation network consisting of 34,666,719 papers and 591,321,826 citations.

Practical implications

Our method is applicable to forecast emerging fields of research subjects in science and helps policymakers to consider science policy.

Originality/value

We calculated PageRank for a giant citation network which is extremely larger than the citation networks investigated by previous researchers.

Abstract

Purpose

Research dynamics have long been a research interest. It is a macro perspective tool for discovering temporal research trends of a certain discipline or subject. A micro perspective of research dynamics, however, concerning a single researcher or a highly cited paper in terms of their citations and “citations of citations” (forward chaining) remains unexplored.

Design/methodology/approach

In this paper, we use a cross-collection topic model to reveal the research dynamics of topic disappearance topic inheritance, and topic innovation in each generation of forward chaining.

Findings

For highly cited work, scientific influence exists in indirect citations. Topic modeling can reveal how long this influence exists in forward chaining, as well as its influence.

Research limitations

This paper measures scientific influence and indirect scientific influence only if the relevant words or phrases are borrowed or used in direct or indirect citations. Paraphrasing or semantically similar concept may be neglected in this research.

Practical implications

This paper demonstrates that a scientific influence exists in indirect citations through its analysis of forward chaining. This can serve as an inspiration on how to adequately evaluate research influence.

Originality

The main contributions of this paper are the following three aspects. First, besides research dynamics of topic inheritance and topic innovation, we model topic disappearance by using a cross-collection topic model. Second, we explore the length and character of the research impact through “citations of citations” content analysis. Finally, we analyze the research dynamics of artificial intelligence researcher Geoffrey Hinton's publications and the topic dynamics of forward chaining.

Abstract

Purpose

This paper proposes a discrimination index method based on the Jain's fairness index to distinguish researchers with the same H-index.

Design/methodology/approach

A validity test is used to measure the correlation of D-offset with the parameters, i.e. H-index, the number of cited papers, the total number of citations, the number of indexed papers, and the number of uncited papers. The correlation test is based on the Saphiro-Wilk method and Pearson's product-moment correlation.

Findings

The result from the discrimination index calculation is a two-digit decimal value called the discrimination-offset (D-offset), with a range of D-offset from 0.00 to 0.99. The result of the correlation value between the D-offset and the number of uncited papers is 0.35, D-offset with the number of indexed papers is 0.24, and the number of cited papers is 0.27. The test provides the result that it is very unlikely that there exists no relationship between the parameters.

Practical implications

For this reason, D-offset is proposed as an additional parameter for H-index to differentiate researchers with the same H-index. The H-index for researchers can be written with the format of “H-index: D-offset”.

Originality/value

D-offset is worthy to be considered as a complement value to add the H-index value. If the D-offset is added in the H-index value, the H-index will have more discrimination power to differentiate the rank of the researchers who have the same H-index.

Abstract

Purpose

Digital literacy and related fields have received interests from scholars and practitioners for more than 20 years; nonetheless, academic communities need to systematically review how the fields have developed. This study aims to investigate the research trends of digital literacy and related concepts since the year of 2000, especially in education.

Design/methodology/approach

The current study analyzes keywords, co-authorship, and cited publications in digital literacy through the scientometric method. The journal articles have been retrieved from the WoS (Web of Science) using four keywords: “Digital literacy,” “ICT literacy,” “information literacy,” and “media literacy.” Further, keywords, publications, and co-authorship are examined and further classified into clusters for more in-depth investigation.

Findings

Digital literacy is a multidisciplinary field that widely embraces literacy, ICT, the Internet, computer skill proficiency, science, nursing, health, and language education. The participants, or study subjects, in digital literacy research range from primary students to professionals, and the co-authorship clusters are distinctive by countries in America and Europe.

Research limitations

This paper analyzes one fixed chunk of a dataset obtained by searching for all four keywords at once. Further studies will retrieve the data from diverse disciplines and will trace the change of the leading research themes by time spans.

Practical implications

To shed light on the findings, using customized digital literacy curriculums and technology is critical for learners at different ages to nurture digital literacy according to their learning aims. They need to cultivate their understanding of the social impact of exploiting technology and computational thinking. To increase the originality of digital literacy-related studies, researchers from different countries and cultures may collaborate to investigate a broader range of digital literacy environments.

Originality/value

The present study reviews research trends in digital literacy and related areas by performing a scientometric study to analyze multidimensional aspects in the fields, including keywords, journal titles, co-authorship, and cited publications.

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