2017
DOI: 10.1016/j.jeap.2016.11.005
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A phraseological exploration of recent mathematics research articles through key phrase frames

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Cited by 61 publications
(33 citation statements)
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References 26 publications
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“…Consulting previous research on the actual cut-off frequency to identify move-markers found that the value for identifying word sequences is somewhat arbitrary but ranges between 10 and 40 instances per million words (Cortes, 2013; Cunningham, 2017). However, the key issue for identifying move-markers is the ‘frequency vs variation’ paradox.…”
Section: Methodsmentioning
confidence: 99%
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“…Consulting previous research on the actual cut-off frequency to identify move-markers found that the value for identifying word sequences is somewhat arbitrary but ranges between 10 and 40 instances per million words (Cortes, 2013; Cunningham, 2017). However, the key issue for identifying move-markers is the ‘frequency vs variation’ paradox.…”
Section: Methodsmentioning
confidence: 99%
“…Additionally, to ensure that word sequences were widely distributed in the corpus, ngrams which were found in at least 75% (19 out of 25) of the journals were selected. The same cut-off point for distributional range has been adopted by previous studies in this area (Cunningham, 2017; Simpson-Vlach and Ellis, 2010).…”
Section: Methodsmentioning
confidence: 99%
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“…The list included n-grams of size four and five, and following the previous research (Grabowski, 2015;Fuster-Markuez & Pennock-Speck, 2015;Römer, 2010), we initially extracted n-grams of size four. Additionally, n-grams of size five were included for two reasons: first, longer n-grams make it possible to extract more semantically complete PFs (Lu et al, 2018); second, n-grams of size five allow us to identify those PFs concerning discipline, genre, and register-specific (Cunningham, 2017;Römer, 2009). With the size of n-grams decided, all the variants of each p-frame were obtained through the software.…”
Section: Identification Of Pfsmentioning
confidence: 99%
“…Discipline-specific lists of words and lexical bundles have been identified for mathematics (Cunningham, 2017), engineering (Hsu, 2014), chemistry (Valipouri & Nassaji, 2013), environmental science (Liu & Han, 2015), pharmacy (Grabowski, 2015), nursing (Yang, 2015), medicine (Wang, Liang, & Ge, 2008), agriculture (Martínez, Beck, & Panza, 2009), and macro groupings of academic subjects along a hard/soft discipline continuum (Durrant, 2017). These and other studies are questioning a general or "one-size-fits-all" approach to teaching academic English.…”
Section: Language Specialist Applied English Centermentioning
confidence: 99%