Indonesian journal of applied linguistics


Table 2  Moves and Steps of the CARS Model Employed in the Corpus Data  Moves_Steps_Number



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Rhetorical styles of Introduction in English langu

Table 2 
Moves and Steps of the CARS Model Employed in the Corpus Data 
Moves_Steps_Number 
of Occurrences 

Move 1: 
Establishing a 
territory 
Claiming centrality 
12 
16
.0
Making topic generalization 
63 
84
.0
Reviewing items of previous research 

0
.0
Move 2: 
Establishing a niche 
Counter-claiming 

0
.0
Indicating a gap 
64 
85.3 
Question-raising 

0
.0
Continuing a tradition 

5.3 
Move 3: 
Occupying the niche 
Outlining purpose/ stating the nature of present research 
40 
53.3 
Listing research questions or hypotheses 
13 
17.3 
Announcing principal findings 

0
.0
Stating the value of the present research 

0
.0
Indicating the structure of research paper 

0
.0
 
As seen in Table 2, not all steps in Move 1 
appear in the corpus data since Step 3 - Reviewing 
items of previous research is omitted. This finding 
contradicts Rahman’s et al. (2017), asserting that 
Step 3 in Move 1 is highly used in the applied 
linguistics 
research 
papers, 
including 
those 
discussing English Language Teaching and English 
for Specific Purposes. Nevertheless, the finding of 
the present research is in line with Suryani et al. 
(2013), who argue that Step 2 of Move 1 is highly 
utilized to begin the introduction section with a 
general statement followed by a more specific 
research topic. Another similar finding to Suryani’s 
et al. (2013) lies in the corpus data in which the 
writers’ first language (L1) is not English. On the 
contrary, the corpus data of Rahman et al. (2017) are 
written by native speakers of English. The 
differences may occur, probably, because English 
native speakers tend to employ a direct thesis 
statement by providing specifications of prior 
findings, attributions to writers, and taking a stance 
by deciding their position (Swales, 1990). 
Meanwhile, non-native speakers of English seem to 
have a strong tendency to portray topic generalization 
emphasized by the complexity of data. 

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