Alexanne Don

The present study aimed at exploring phrasal complexity features in data commentaries produced by graduate students and in research articles written by expert writers. To this end, 25 empirical RAs in the field of Applied Linguistics and 158 data commentaries generated by graduate students of English Language Teaching were comparatively examined. The results revealed that students approximated expert writers in terms of producing two linguistic features (i.e., N+N structures and nominalizations). However, they differed significantly from expert writers in generating four linguistic elements (i.e., attributive adjectives, appositive structures, of -genitives, and PPs as noun post-modifiers). The results also revealed that expert writers’ texts comprise varied presence of exceedingly complex patterns of pre-modification, triple/quadruple/quintuple (pre)modification, a hybrid of novel appositive structures, and multiword hyphenated adjectives. Conversely, graduate students’ language could be characterized by less variety, single/dual (pre)modification, a far less extensive range of noun-participle compounds functioning as nominal pre-modifiers, linguistically limited complex modifications, and minimally multifarious patterns of use associated with N+N formulations. Overall, the findings can give fresh insights into the needs of the L2 student writers in developing an academic text.

<span>The present study aimed to explore how nominalization is manifested in a sample of Physics and Applied Linguistics research articles (RAs), representing hard and soft sciences respectively. To this end, 60 RAs from discipline-related professional journals were randomly selected and analyzed in light of Halliday and Matthiessen’s (1999) taxonomy of nominalization. Comparing the normalized frequencies indicated that articles in Applied Linguistics differ significantly from their counterparts in Physics as they include more nominalized expressions. Moreover, the analysis brought out the findings that deployment of nominalization Type Two is significantly different from the other three types of nominalization in each discipline. Subsequently, the obtained expressions were put into their context of use in order to extract the most prevalent patterns of nominalization in the RAs. The investigation into the embedded patterns introduced 15 common patterns for Physics and Applied Linguistics RAs. Chi-square analyses suggested statistically significant differences in using only four patterns. Finally, implications accrue to the findings in reference to academic writing teachers and course designers.</span>