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Subject dislocation in Ontario English: Insights from sociolinguistic typology

Published online by Cambridge University Press:  06 November 2023

Sali A. Tagliamonte*
Affiliation:
University of Toronto, Canada
Bridget L. Jankowski
Affiliation:
University of Toronto, Canada
*
Corresponding author: Sali A. Tagliamonte. Email: sali.tagliamonte@utoronto.ca
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Abstract

Subject dislocation (SD) is common across languages. In French, it is a vernacular norm. In English, it is comparatively rare. This article examines English SD in a unique contrastive situation in Ontario, Canada: two communities where SD is a community norm, one where individuals speak both English and French (Kapuskasing), and the other where the population speaks English only (Parry Sound). Dislocated subjects are produced by the same underlying linguistic mechanisms in both places, with parallel constraints by type of subject and intervening material, suggesting a typological universal. However, SD is age-graded in Kapuskasing, regardless of heritage language. In Parry Sound, it is obsolescent, in steady decline over the twentieth century. We conclude that while typological trends are underlain by universal cognitive processes, locally embedded sociocultural influences are the source of differentiation.

Information

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press.
Figure 0

Figure 1. Map showing Kapuskasing and Parry Sound (Adapted from: https://commons.wikimedia.org/wiki/File:Ontario_Locator_Map.svg).

Figure 1

Table 1. Kapuskasing and Parry Sound corpora

Figure 2

Table 2. Proportion of SD with NP subjects by cohort

Figure 3

Figure 2. Proportion of SD by cohort.

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Figure 3. Proportion of SD by gender and cohort.

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Figure 4. Proportion of SD by occupation and cohort.

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Figure 5. Proportion of SD by education and cohort.

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Figure 6. Proportion of SD by decade of birth and cohort.

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Figure 7. Proportion of SD by type of subject, decade of birth, and cohort.

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Figure 8. Proportion of SD by intervening material, cohort, and decade of birth.

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Figure 9. Random forest analysis by cohort and community.

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Figure 10. Panel (a). Conditional inference tree plotting linguistic and social factors with no intervening material. Panel (b). Conditional inference tree plotting linguistic and social factors with intervening material.

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Table 3. Mixed-effects logistic regression for the use of SD