Criticism is inherently impolite and a face-threatening act generally leading to conflicts among interlocutors. It is equally challenging for both native and non-native speakers, and needs pre-planning before performing it. The current research examines the production of non-institutional criticism by Iraqi EFL university learners and American native speakers. More specifically, it explores to what extent Iraqi EFL learners and American native speakers vary in (i) performing criticism, (ii) mitigating criticism, and (iii) their pragmatic choices according to the contextual variables of power and distance. To collect data, a discourse-completion task was used to elicit written data from 20 Iraqi EFL learners and 20 American native speakers. Findings revealed that though both groups regularly used all strategy types, Iraqi EFL learners criticized differently from American speakers. When expressing criticism, Iraqi learners tended to be indirect whereas American speakers tended to be direct. In mitigating their criticism, Iraqi learners were significantly different from American speakers in their use of internal and external modifiers. Furthermore, both groups substantially varied their pragmatic choices according to context. The differences in their pragmatic performance could be attributed to a number of interplaying factors such as EFL learners’ limited linguistic and pragmatic knowledge, the context of learning and L1 pragmatic transfer. Finally, a number of conclusions and pedagogical implications are presented.
In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the
... Show MoreThe theatrical show has gone through a lot of changes where the actor was the most significant factor in all the theatrical shows since the very beginning of the art of acting by the Greeks until the present day. The actor went through many stages that employed his tools in different ways. The body in the theatre had a great importance because it is the perceived physical element that creates the communication between the actor and the audience in the theatrical show. The actor's body had a special language that carries different meanings and creates the communication between the actor and the audience in the theatrical show. The audience can decipher the codes of that body, thus, the researcher found the compatibility and differe
... Show MoreRA Ali, LK Abood, Int J Sci Res, 2017 - Cited by 2
Abstract
The study aims to examine the relationships between cognitive absorption and E-Learning readiness in the preparatory stage. The study sample consisted of (190) students who were chosen randomly. The Researcher has developed the cognitive absorption and E-Learning readiness scales. A correlational descriptive approach was adopted. The research revealed that there is a positive statistical relationship between cognitive absorption and eLearning readiness.
The high cost of chemical analysis of water has necessitated various researches into finding alternative method of determining portable water quality. This paper is aimed at modelling the turbidity value as a water quality parameter. Mathematical models for turbidity removal were developed based on the relationships between water turbidity and other water criteria. Results showed that the turbidity of water is the cumulative effect of the individual parameters/factors affecting the system. A model equation for the evaluation and prediction of a clarifier’s performance was developed:
Model: T = T0(-1.36729 + 0.037101∙10λpH + 0.048928t + 0.00741387∙alk)
The developed model will aid the predictiv
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