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“Strengthening Connections with the Audience: Reformation and Exemplification in Mathematics Research Articles”

by Kristy Lesperance | Xchanges 11.2

Method

Corpus Selection

A corpus of scholarly, peer-reviewed, online-access articles was constructed following the “select, analyze, select again” procedure outlined by Bauer & Aarts (2000, p. 23). Journals were chosen within the top ranking American journals as listed on the SCImago (2007) website. Two journals were selected to represent “hard, pure” theoretical mathematics (hereafter “theory”): the Journal of the American Mathematical Society and SIAM Review. Two journals were also selected to represent “soft, applied” mathematics education (hereafter “education”): the Journal for Research in Mathematics Education and Research in Mathematics Education. Individual articles were then selected which included geometry or geometric in the title, but restricted to those with the observed median length of 15 to 35 pages. This resulted in a corpus of 29 articles spanning nearly 40 years: 17 representing theory with a combined 192,200 words, and 12 representing education with a combined 103,279 words.

Corpus Analysis

The list of code glosses provided by Hyland (2007) was used to search for target words within the corpus articles (see Table 1 below), as well as some additional target words that appeared frequently in the present corpus, but which were not discussed by Hyland. Each article was examined using the PDF text-search feature. Each occurrence was evaluated to identify the target function of the term within the sentence in which it was found.

Table 1: Frequently used code gloss markers (frequency per 100,000 words)

Code gloss marker Theory articles Education articles
Reformulation markers
in particular 5.62 3.00
or (x) 4.68 6.10
i.e. 4.21 2.71
that is 3.28 2.42
X means 1.93 0.48
in other words 1.56 1.16
meaning/mean 0.88 0.39
equivalently 0.88 0
essentially 0.47 0
specifically 0.36 1.07
especially 0.31 1.65
Exemplification markers
such as 0.88 6.88
for example 3.07 5.91
like 1.72 3.39
e.g. 0.83 3.39
for instance 1.51 1.65
an example of 0.52 1.36
counterexample(s) 0.62 0.39
say 2.24 0.39