Search (5 results, page 1 of 1)

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Abstract

The term author co-citation is defined and classified according to four distinct forms: the pure first-author co-citation, the pure author co-citation, the general author co-citation, and the special co-authorlco-citation. Each form can be used to obtain one count in an author co-citation study, based an a binary counting rule, which either recognizes the co-citedness of two authors in a given reference list (1) or does not (0). Most studies using author co-citations have relied solely an first-author cocitation counts as evidence of an author's oeuvre or body of work contributed to a research field. In this article, we argue that an author's contribution to a selected field of study should not be limited, but should be based an his/her complete list of publications, regardless of author ranking. We discuss the implications associated with using each co-citation form and show where simple first-author co-citations fit within our classification scheme. Examples are given to substantiate each author co-citation form defined in our classification, including a set of sample Dialog(TM) searches using references extracted from the SciSearch database.

Source

Journal of the American Society for Information Science and Technology. 55(2004) no.6, S.513-529

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Abstract

A theory of "megacitation" is introduced and used in an experiment to demonstrate how a qualitative scholarly book review can be converted into a weighted bibliometric indicator. We employ a manual human-coding approach to classify book reviews in the field of history based on reviewers' assessments of a book author's scholarly credibility (SC) and writing style (WS). In total, 100 book reviews were selected from the American Historical Review and coded for their positive/negative valence on these two dimensions. Most were coded as positive (68% for SC and 47% for WS), and there was also a small positive correlation between SC and WS (r = 0.2). We then constructed a classifier, combining both manual design and machine learning, to categorize sentiment-based sentences in history book reviews. The machine classifier produced a matched accuracy (matched to the human coding) of approximately 75% for SC and 64% for WS. WS was found to be more difficult to classify by machine than SC because of the reviewers' use of more subtle language. With further training data, a machine-learning approach could be useful for automatically classifying a large number of history book reviews at once. Weighted megacitations can be especially valuable if they are used in conjunction with regular book/journal citations, and "libcitations" (i.e., library holding counts) for a comprehensive assessment of a book/monograph's scholarly impact.

Source

Journal of the Association for Information Science and Technology. 65(2014) no.11, S.2248-2260

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Source

Journal of the American Society for Information Science and Technology. 57(2006) no.11, S.1487-1502

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Source

Journal of the American Society for Information Science and Technology. 62(2011) no.10, S.1979-1991

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Source

Journal of the Association for Information Science and Technology. 66(2015) no.7, S.1333-1347