Linguistic pet peeves
Linguistic pet peeves are words that people misuse when communicating. Some people use words and phrases which seem to annoy others more. For instance, using a word like incentivize instead of motivate annoys me more. Another word that drives me crazy is when people add an n to the word other when using it after the word whole.An example of the sentence with an n added to the word other is when someone says “that’s a whole nother matter”. It really irritates me. I wonder why use the term nother other than using other which seems to be easier and understandable. Another word is when someone uses word past history when referring to something that happened before. That is not right according to me. However, there is a correlation between linguistic pet peeves and ecological perspective as well as the prescriptivism perspective and this relation is explained below.
Linguistic and ecological perspective is portrayed in different tribes in the world. despite its benefits, the perspective is rejected in the education systems of many countries whereby they undermine the culture and language of different students.
Linguistic and prescriptivism perspective is usually practiced by nonacademic linguists, whereas modern academic linguists, limits themselves to study the legal framework of language and importance. The term prescriptivism originates from the word prescription which occurs in the literature on the subject. The terms prescriptivism and prescription relate to each other. Prescription means those people practicing prescriptivism.Studies on prescriptivism are exercised in the regions where people speak English. Wang.
- References
- del Pinal, E. H. (2019). Reading Laudato Si’in the Verapaz: A Case of Localizing Catholic Teachings. Exchange, 48(3), 291-301.
- Wang, W. Y., & Yang, D. (2015, September). That’s so annoying!!!: A lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using# petpeeve tweets. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (pp. 2557-2563.