Abstract
The article discusses the process of textually mediated communication in science and proposes an approach that complements citation analysis. Namely, it addresses the question of how the author’s text is read by the reader and whether the reader interprets the text in the same manner as the author. Fifty-seven scholarly contributions (articles, book chapters and book reviews), written by three social scientists, were content analyzed with the help of the QDA Miner and WordStat computer programs. The outcomes of the qualitative coding were compared with the outcomes of the analysis of word co-occurrences and the outcomes of the analysis on the basis of a dictionary based on substitution. Our findings suggest that texts have plural interpretations. Depending on the reading context, either the author’s or the reader’s perspective prevails. Also, both the author and the reader may read the text in a either deep or perfunctory manner. Deep reading necessitates spending significant time and cognitive resources.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
The search was conducted on April 25, 2017 using the search terms TOPIC: (“adult reading”), TOPIC: (“school reading”) etc.
Table 2 contains the squared distances between the Reader’s individual codebook and the common codebook. The method of least squares (the linear least squares fitting technique) is a standard approach to the approximate solution of over-determined systems, i.e., sets of equations in which there are more equations than unknowns. “Least squares” means that the overall solution minimizes the sum of the squares of the errors made in the results of every single equation (the OLS—Ordinary Least Squares criterion; Warner 2008, p. 52). In this particular case the common codebook was represented in the form of a 15 × 15 matrix for A’s texts (because it contained 15 codes for content analyzing them), a 9 × 9 matrix for B.’s texts and a 13 × 13 matrix for analyzing C’s texts. These “ideal” matrixes were compared with the matrixes obtained as a result of transforming the participants’ individual codebooks. For instance, A’s matrix referring to A’s texts had 9 rows, which corresponds to the number of codes in A’s individual codebook, and 15 columns, which corresponds to the number of codes in the common codebook. B’s matrix referring to C’s texts had 55 rows and 13 columns and so forth. Each cell contained either 1 (if a code from the Reader’s codebook matches that from the common codebook) or 0. In the case of two perfectly identical matrixes, the sum of squared distances is equal to 0. In all other cases (there is a mismatch between the number rows or there is more than one code corresponding to a code in the common codebook), it exceeds 0. The formula for calculating the sum of squared distances contains the following components: \(\sum {(M - \overline{M} )^{2} } = \sum\nolimits_{i = 1}^{n} {(x_{i} - \overline{x}_{i} )^{2} + \sum\nolimits_{j = 1}^{m} {(x_{j} - \overline{x}_{j} )^{2} } }\), where \(M\) refers to a particular Reader’s matrix, \(\overline{M}\)—to the common matrix (with the common codebook at its origin), \(x_{i}\)—to the column i mean in \(M\), \(\overline{x}_{i}\)—to the column i mean in an “ideal” matrix perfectly matching \(\overline{M}\), \(x_{j}\)—to the row j mean in \(M\), \(\overline{x}_{j}\)—to the row j mean in \(\overline{M}\), n—to the number of columns in \(M\), m—to the number of rows in \(M\). \(\overline{x}_{i}\) and \(\overline{x}_{j}\) depends on the number of codes in the common codebook. For instance, if the common codebook contains 5 codes, an “ideal” matrix perfectly matching it has 5 columns and 5 rows: \(\overline{x}_{i} = \overline{x}_{j} = \frac{1}{5} = 0.2\) (because each column and row contains one non-empty cell).
The same arguably applies to the Reader, but the present research design does not allow capturing changes in the Reader’s take on a text over time.
When assessing these values of Krippendorff’s α, one has to bear in mind that the suggested cut-off points, .667 and .800 (Krippendorff 2004, p. 241), do not take into account, on one hand, an inverse relationship between the values of α and the number of coding categories, 37 in the present case (Muñoz-Leiva et al. 2006; Oleinik et al. 2014) and, on the other hand, the fact that coded units are not given or natural here. The Readers applied codes to particular segments of the text, as opposed to the text as a whole (values of Krippendorff’s α are higher in the latter case). No known computer program allows calculating the coefficients of agreement separately for coding and for unitizing where unitizing refers to partitioning of a given continuum into sections (Krippendorff 2004, pp. 251–256; see also Oleinik 2010, p. 872).
This means that C’s texts tend to be relatively more readable and A’s texts relatively less readable. The use of the formula for calculating the Flesch score that was not specifically adapted to Russian language does not prevent one from assessing the relative readability of texts. For instance, the mean readability score for a non-random sample of articles in Russian available at three major news websites was −32.03; the mean readability of Leo Tolstoy’s tales for children was +23.51.
References
Abbott, R. (2013). Crossing thresholds in academic reading. Innovations in Education and Teaching International, 50(2), 191–201.
Auerbach, B. (2006). «Publish and Perish»: La définition légitime des sciences sociales au prisme du débat sur la crise de l’édition SHS. Actes de la recherche en sciences sociales, 164, 75–92.
Bakhtin, M. (1979). Problemy poetiki Dostoevskogo [Problems of Dostoevsky’s Poetics] (4th ed.). Moscow: Sovetskaya Rossiya.
Bourdieu, P. (1984). Homo academicus (Paris ed.). Paris: de Minuit.
Brossard, D., & Shanahan, J. (2006). Do they know what they read? Building a scientific literacy measurement instrument based on science media coverage. Science Communication, 28(1), 47–63.
Budd, J. M. (2001). Misreading science in the twentieth century. Science Communication, 22(3), 300–315.
Fischhoff, B. (2013). The sciences of science communication. Proceedings of the National Academy of Sciences of the United States of America, 110(3), 14033–14039.
Flyvbjerg, B. (2006). Five misunderstandings about case-study research. Qualitative Inquiry, 12(2), 219–245.
Goldgar, A. (1995). Impolite learning: Conduct and community in the Republic of Letters, 1680–1750. New Haven: Yale University Press.
Gryaznova, Yu., & Ratz, M. (2008). Kak nam chitat’? [How shall we read?]. Voprosy psykholingvistiki, 8, 142–146.
Hartley, J. (2006). Reading and writing book reviews across disciplines. Journal of the American Society for Information Science and Technology, 57(9), 1194–1207.
Hartley, J. (2016). Is time up for the Flesch measure of reading ease? Scientometrics, 107(3), 1523–1526.
Hartley, J., & Cabanac, G. (2015). An academic odyssey: Writing over time. Scientometrics, 103(3), 1073–1082.
Hartley, J., Sotto, E., & Fox, C. (2004). Clarity across the disciplines: An analysis of texts in the sciences, social sciences, and arts and humanities. Science Communication, 26(2), 188–210.
Harwood, N. (2009). An interview-based study of the functions of citations in academic writing across two disciplines. Journal of Pragmatics, 41(3), 497–518.
Henige, D. P. (2006). Discouraging verification: Citation practices across the disciplines. Journal of Scholarly Publishing, 37(2), 99–118.
Hirschauer, S. (2010). Editorial Judgments: A praxeology of “Voting” in peer review. Social Studies of Science, 40(1), 71–103.
Hultberg, J. (1997). The two cultures revised. Science Communication, 18(3), 194–215.
Hyland, K. (2004 [2000]). Disciplinary discourses: Social interactions in academic writing. Ann Arbor: The University of Michigan Press.
Krippendorff, K. (2004). Content analysis: An introduction to its methodology. Thousand Oaks, CA: Sage.
Latour, B. (1987). Science in Action: How to follow scientists and engineers through society. Cambridge, MA: Harvard University Press.
Littmann, M. (2005). Courses in science writing as literature. Public Understanding of Science, 14(1), 103–112.
Muñoz-Leiva, F., Montoro-Ríos, F. J., & Luque-Martínez, T. (2006). Assessment of interjudge reliability in the open-ended questions coding process. Quality and Quantity, 40(4), 519–537.
Norris, S. P., & Philips, L. M. (1994). The relevance of a reader’s knowledge within a perspectival view of reading. Journal of Reading Behavior, 26(4), 391–412.
Oleinik, A. (2010). Mixing quantitative and qualitative content analysis: Triangulation at work. Quality and Quantity, 45(4), 859–873.
Oleinik, A. (2012). Publication patterns in Russia and the West compared. Scientometrics, 93(2), 533–551.
Oleinik, A., Popova, I., Kirdina, S., & Shatalova, T. (2014). On the choice of measures of reliability and validity in the content-analysis of texts. Quality and Quantity, 48(5), 2703–2718.
Sapir, E. (1961). Culture, language and personality. Berkeley: University of California Press.
Schelling, T. S. (1960). The strategy of conflict. Cambridge, MA: Harvard University Press.
Simon, H. A. (1978). Rationality as process and as product of thought. American Economic Review, 68(2), 2–16.
Skinner, Q. (2002). Visions of politics: Regarding method (Vol. 1). Cambridge: Cambridge University Press.
Spektor-Levy, O., Eylon, B.-S., & Schrez, Z. (2009). Teaching scientific communication skills in science studies: Does it make a difference? International Journal of Science and Mathematics Education, 7(5), 875–903.
Stanovich, K. E. (2003). Understanding the styles of science in the study of reading. Scientific Studies of Reading, 7(2), 105–126.
Stremersch, S., Verniers, I., & Verhoef, P. C. (2007). The quest for citations: Drivers of article impact. Journal of Marketing, 71(3), 171–193.
Volentine, R., & Tenopir, C. (2013). Value of academic reading and value of the library in academics’ own words. Aslib Proceedings: New Information Perspectives, 65(4), 425–440.
Warner, R. (2008). Applied statistics: From bivariate through multivariate techniques. Thousand Oaks, CA: Sage.
Weber, M. (1968). Economy and society: An outline of interpretative sociology. New York: Bedminster Press.
Weigle, S. C., Yang, W. W., & Montee, M. (2013). Exploring reading processes in an academic reading test using short-answer questions. Language Assessment Quarterly, 10(1), 28–48.
White, H. D. (2011). Relevance theory and citations. Journal of Pragmatics, 43(14), 3345–3361.
Whorf, B. L. (1956). Language, thought and reality: Selected writings. Cambridge, MA: MIT Press.
Wilson, C. S., & Tenopir, C. (2008). Local citation analysis, publishing and reading patterns: Using multiple methods to evaluate faculty use of an academic library’s research collection. Journal of the American Society for Information Science and Technology, 59(9), 1393–1408.
Yore, L. D., Hand, B. M., & Florence, M. K. (2004). Scientists’ views of science, models of writing, and science writing practices. Journal of research in science and teaching, 41(4), 338–369.
Yore, L. D., Hand, B. M., & Prain, V. (2002). Scientists as writers. Science Education, 86(5), 672–692.
Acknowledgements
The authors are grateful for the Scientometrics anonymous reviewers’ deep and constructive reading of the text. An earlier, Russian, version of the text appeared in SOCIS (Sotsiologicheskie Issledovaniia). The remaining errors and inaccuracies are their own.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Oleinik, A., Kirdina-Chandler, S., Popova, I. et al. On academic reading: citation patterns and beyond. Scientometrics 113, 417–435 (2017). https://doi.org/10.1007/s11192-017-2466-z
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-017-2466-z