A Content Analysis of Book Reviews in the AJS, ASR, and Social Forces

Champion, Dean J.; Morris, Michael

doi:10.1086/225431

Cited by 31 publications

(16 citation statements)

References 3 publications

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“…And, of course, there are several studies within single disciplines where disciplinary contrasts are not required. For example, Nicolaisen (2002a) and Riley and Spreitzer (1970) contrasted reviews in social science journals, Champion and Morris (1973) and Snizek and Fuhrman (1979) compared reviews in sociology journals, Schubert, Zsindely, Telcs, and Braun (1984) studied book reviews in chemistry, and Gastel (1993) examined book reviews in medical journals. Indeed, some authors examine the book reviews published in a single journal (e.g., Bressler, 1999; Doessel, 2003).…”

Section: Reviews Across the Disciplinesmentioning

confidence: 99%

“…Several studies have noted that book reviews tend to be more positive than negative in their evaluations of the books in question (e.g., Bilhartz, 1984; Carlo & Natowitz, 1996; Champion & Morris, 1973). Moore (1978) reported sex differences here in that both men and women reviewers (in psychology) favored elements of books written by members of their own sex more than they did those written by the other sex, and that reviewers of both sexes found more negative elements in books written by men.…”

Section: The Language Of Book Reviewsmentioning

confidence: 99%

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Reading and writing book reviews across the disciplines

Hartley

2006

J. Am. Soc. Inf. Sci.

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Reading and writing book reviews for learned journals plays an important part in academic life but little is known about how academics carry out these tasks. The aim of this research was to explore these activities with academics from the arts and humanities, the social sciences, and the natural sciences. An electronic questionnaire was used to ascertain (a) how often the respondents read and wrote book reviews, (b) how useful they found them, and (c) what features they thought important in book reviews. Fifty-two academics in the arts, 53 in the social sciences, and 51 in the sciences replied. There were few disciplinary differences. Most respondents reported reading between one and five book reviews a month and writing between one and two a year. There was high overall agreement between what the respondents thought were important features of book reviews, but there were also wide individual differences between them. This agreement across the disciplines supports the notion that book reviews can be seen as an academic genre with measurable features. This has implications for how they are written, and how authors might be taught to write them better. A potential checklist for authors is suggested.

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Section: Reviews Across the Disciplinesmentioning

confidence: 99%

Section: The Language Of Book Reviewsmentioning

confidence: 99%

Reading and writing book reviews across the disciplines

Hartley

2006

J. Am. Soc. Inf. Sci.

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“…The many‐parts‐to‐one notion implies that the content of the review, like the citation proper, is a suitable element for in‐depth study, and, indeed, content analyses of book reviews were carried out as early as the 1970s (Champion & Morris, ; Riley & Spreitzer, ; Snizek & Fuhrman, ). During this period, Glenn () claimed that reviews were “not as adequate for evaluating books and authors as many people seem[ed] to think” (p. 254).…”

Section: Literature Reviewmentioning

confidence: 99%

A machine‐learning approach to coding book reviews as quality indicators: Toward a theory of megacitation

Zuccala

Someren

Bellen

2014

Asso for Info Science & Tech

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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.

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“…The Poisson model has been suggested as providing a satisfactory description of the occurrence frequencies of natural language terms [12,23,36]. Use of this distribution may be helpful if advantage is taken of content analytic methods for counting the number of times particular positive or negative terms or expressions occur in a message [6,18]. Because few short messages have more than a single occurrence of each content-bearing word, the Poisson distribution is most effectively used for lengthy messages.…”

Section: Poisson Independently Distributed Featuresmentioning

confidence: 99%

Minimizing information overload: the ranking of electronic messages

Losee¹

1989

Journal of Studies in International Education

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The decision to examine a message at a particular point in time should be made rationally and economically if the message recipient is to operate efficiently. Electronic message distribution systems, electronic bulletin board systems, and telephone systems capable of leaving digitized voice messages can contribute to "information overload," defined as the economic loss associated with the examination of a number of non-or less-relevant messages. Our model provides a formal method for minimizing expected information overload.The proposed adaptive model predicts the usefulness of a message based on the available message features and may be used to rank messages by expected importance or economic worth. The assumptions of binary and two Poisson independent probabilistic distributions of message feature frequencies are examined, and methods of incorporating these distributions into the ranking model are examined. Ways to incorporate user supplied relevance feedback are suggested. Analytic performance measures are proposed to predict system quality. Other message handling models, including rule based expert systems, are seen as special cases of the model. The performance is given for a set of UNIX shell programs which rank messages. Problems with the use of this formal model are examined, and areas for future research are suggested.

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A Content Analysis of Book Reviews in the AJS, ASR, and Social Forces

Cited by 31 publications

References 3 publications

Reading and writing book reviews across the disciplines

Reading and writing book reviews across the disciplines

A machine‐learning approach to coding book reviews as quality indicators: Toward a theory of megacitation

Minimizing information overload: the ranking of electronic messages

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