In studying actual Web searching by the public at large, we analyzed over one million Web queries by users of the Excite search engine. We found that most people use few search terms, few modified queries, view few Web pages, and rarely use advanced search features. A small number of search terms are used with high frequency, and a great many terms are unique; the language of Web queries is distinctive. Queries about recreation and entertainment rank highest. Findings are compared to data from two other large studies of Web queries. This study provides an insight into the public practices and choices in Web searching.
Open peer review (OPR), where review reports and reviewers’ identities are published alongside the articles, represents one of the last aspects of the open science movement to be widely embraced, although its adoption has been growing since the turn of the century. This study provides the first comprehensive investigation of OPR adoption, its early adopters and the implementation approaches used. Current bibliographic databases do not systematically index OPR journals, nor do the OPR journals clearly state their policies on open identities and open reports. Using various methods, we identified 617 OPR journals that published at least one article with open identities or open reports as of 2019 and analyzed their wide-ranging implementations to derive emerging OPR practices. The findings suggest that: (1) there has been a steady growth in OPR adoption since 2001, when 38 journals initially adopted OPR, with more rapid growth since 2017; (2) OPR adoption is most prevalent in medical and scientific disciplines (79.9%); (3) five publishers are responsible for 81% of the identified OPR journals; (4) early adopter publishers have implemented OPR in different ways, resulting in different levels of transparency. Across the variations in OPR implementations, two important factors define the degree of transparency: open identities and open reports. Open identities may include reviewer names and affiliation as well as credentials; open reports may include timestamped review histories consisting of referee reports and author rebuttals or a letter from the editor integrating reviewers’ comments. When and where open reports can be accessed are also important factors indicating the OPR transparency level. Publishers of optional OPR journals should add metric data in their annual status reports.
Relationships between authors based on characteristics of published literature have been studied for decades.Author cocitation analysis using mapping techniques has been most frequently used to study how closely two authors are thought to be in intellectual space based on how members of the research community co-cite their works. Other approaches exist to study author relatedness based more directly on the text of their published works. In this study we present static and dynamic wordbased approaches using vector space modeling, as well as a topic-based approach based on latent Dirichlet allocation for mapping author research relatedness. Vector space modeling is used to define an author space consisting of works by a given author. Outcomes for the two word-based approaches and a topic-based approach for 50 prolific authors in library and information science are compared with more traditional author cocitation analysis using multidimensional scaling and hierarchical cluster analysis. The two word-based approaches produced similar outcomes except where two authors were frequent co-authors for the majority of their articles. The topic-based approach produced the most distinctive map.This literature review section covers two parts. The first section reviews existing techniques used for mapping bibliometric units. The second section briefly reviews the relevant models used in the study. It includes an introduction to the essential ideas of the vector space model, how it applies to the current study, and provides a short introduction to the LDA or topic model.
Bibliometric Relatedness MeasuresMany bibliometric studies have formulated quantitative measures to map scientific structure at different levels of granularity including authors, articles, and journals. In reviewing visualization studies for knowledge domains, Börner, Chen, and Boyack (2005) categorized relatedness measures into two broad categories: citation linkages and co-occurrence similarities. Within the relatedness measures, five basic approaches were identified: direct citation, cocitation analysis, co-authorship analysis, bibliographic coupling, and co-word analysis.Direct citation. Direct citation accounts for the relatedness between a citing work and a cited work based on citing behavior. This measure is usually asymmetric. Shibata, Kajikawa, Takeda, and Matsushima (2008) explored citation networks for two research domains and divided the networks into clusters in order to identify research fronts. Direct citation has not attracted wide attention. One possible reason may be its requirement for a very long time window to obtain a sufficient linking signal for clustering (
The investigators studied author research impact using the number of citers per publication an author's research has been able to attract, as opposed to the more traditional measure of citations. A focus on citers provides a complementary measure of an author's reach or influence in a field, whereas citations, although possibly numerous, may not reflect this reach, particularly if many citations are received from a small number of citers. In this exploratory study, Web of Science was used to tally citer and citation-based counts for 25 highly cited researchers in information studies in the United States and 26 highly cited researchers from the United Kingdom. Outcomes of the tallies based on several measures, including an introduced ch-index, were used to determine whether differences arise in author rankings when using citer-based versus citation-based counts. The findings indicate a strong correlation between some citation and citer-based measures, but not with others. The findings of the study have implications for the way authors' research impact may be assessed.
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