| Summary: | To date there has been limited research into the language regard of Canadians towards the varieties of English spoken across this vast country. This thesis provides a comprehensive investigation of the language regard of English-speaking Canadians towards varieties of Canadian English, alongside a variationist study of 13 previously studied lexical variables and 10 new lexical variables. This research on perception complements previous work on production, to build a better understanding of sociolinguistic variation (see Kretzschmar, 2000 and Preston, 2018). The methodology provides insights into the use of an online map task with the current available tools, while addressing the strength and weaknesses of these tools. An online survey allowed for data to be gathered from all areas of Canada and for simultaneous collection and analysis of lexical and perceptual data. This study includes a content analysis using GIS technology; an analysis of rating tasks for regions on three characteristics: correctness, pleasantness, and similarity; an experimental rating task focusing on stereotypes of provinces; supplementary perceptual data; and a lexical variation component. Data from 192 completed lexical surveys were analyzed using total variation, net variation, and major isoglosses to help further develop the understanding of the sociolinguistic landscape of Canadian English. Findings suggest that Canadians from different regions harbour perceptions towards Canadian English based on their region of origin, with some areas (e.g., Newfoundland and Labrador, and Québec) appearing more salient to participants than others. The findings from the analysis of the lexical data echo previous findings (e.g., Boberg, 2010, 2016; Gallinger & Motskin, 2018) while also highlighting regional variation in some variables that have not previously been studied, suggesting further research is needed focusing on these variables. Overall, the results demonstrate the advantages and disadvantages of an online study to survey a large number ...
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