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How are journals cited? characterizing journal citations by type of citation

2021-02-22 · Domenic Rosati

Evaluation of journals for quality is one of the dominant themes of bibliometrics since journals are the primary venue of vetting and distribution of scholarship. There are many criticisms of quantifying journal impact with bibliometrics including disciplinary differences among journals, what source materials are used, time windows for the inclusion of works to measure, and skewness of citation distributions (Lariviere & Sugimoto, 2019). However, despite various attempts to remediate these in newly proposed indicators such as SJR, SNIP, and Eigenfactor (Walters, 2017) indicators still remain based on citation counts and fail to acknowledge the critical differences that the type of citation made, whether it's supporting or disputing a work when quantifying journal impact. While various programs have been suggested to apply and encompass citation content analysis within bibliometrics projects, citation content analysis has not been done at the scale needed in order to supplement quantitate journal citation analysis until the scite citation index was produced. Using this citation index containing citation types based on citation function (supporting, disputing, or mentioning) we present initial results on the statistical characterization of citations to journals based on citation function. We also present initial results of characterizing the ratio of supports and disputes received by a journal as a potential indicator of quality and show two interesting results: the ratio of supports and disputes do not correlate with total citations and that the distribution of this ratio is not skewed showing a normal distribution. We conclude with a proposal for future research using citation analysis qualified by citation function as well as the implications of performing bibliometrics tasks such as research evaluation and information retrieval using citation function.

📄 PDF Abstract BibTeX arXiv:2102.11043

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Information RetrievalRetrievalVocal Bursts Type Prediction

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SNIP SNIP, or Scale Normalization for Image Pyramids, is a multi-scale training scheme that selectively back-propagates the gradients of object instances of different sizes as…

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