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Mapping Academic Values in the Disciplines

A Corpus-Based Approach


Davide Simone Giannoni

A broad strand of applied linguistic research has focused on the language of science and scholarship, stressing its role in the construction and negotiation of knowledge claims. Central to the success of such texts is the use of evaluative expressions encoding what is considered to be desirable or undesirable in a given domain. While the speech acts relevant to evaluation have been extensively researched, little is known of the underlying values they encode. This volume seeks to fill the gap by exploring the main facets of academic value in a corpus of research articles from leading journals in anthropology, biology, computer science, economics, engineering, history, mathematics, medicine, physics and sociology. The collocations and qualified entities associated with such variables in the corpus provide insights into how scholars draw on a repertoire of conventional, largely unqualified, axiological meanings instrumental to the production of new knowledge in their field.


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4. Methodology 73


73 4. Methodology 4.1. Corpus data The files assembled as described in the previous chapter were grouped by domain and uploaded to a well-known concordancing application – WordSmith Tools, hence WST (Scott 2006) – for textual data extraction. After generating a wordlist (WL) for each set of texts it was possible to access a number of quantitative parameters (as listed in the Statistics window) across the corpus, which totals almost one million words. Detailed data for each text are listed in Appendix 2, while the table below shows how such parameters vary across disciplines. Tokens WL tokens Types TTR STTR Sentence length ANTH 85,958 81,547 7,004 8.59 36.42 23.52 BIO 79,891 74,199 6,495 8.75 35.52 26.49 CS 138,038 133,408 8,244 6.18 37.20 23.87 ECO 106,370 103,399 7,220 6.98 36.63 24.28 ENG 69,885 65,706 5,765 8.77 34.28 26.44 HIST 106,393 105,399 11,757 11.15 45.93 25.55 MATH 171,872 155,407 4,925 3.17 24.71 16.21 MED 39,973 36,846 3,837 10.41 33.29 30.18 PHY 63,032 59,325 3,646 6.15 31.77 26.77 SOC 124,773 121,311 9,513 7.84 41.26 27.51 Overall 986,185 936,547 33,073 3.53 36.10 23.32 Table 1. Corpus data by discipline. The first column gives the total number of tokens found in each 10- text set. This is followed by a smaller figure, showing the number of tokens actually used in the WL to calculate...

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