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Cognitive Linguistics
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when variables align a bayesian multinomial mixed effects model of english permissive constructions

Authors: Levshina, Natalia;

when variables align a bayesian multinomial mixed effects model of english permissive constructions

Abstract

AbstractThis paper is a quantitative multifactorial study of the near-synonymous constructionslet+V,allow+toV andpermit+toV based on the British National Corpus. The study investigates the differences between these constructions with the help of 23 formal, semantic, social and collostructional variables. A Bayesian multinomial mixed-effects model reveals a remarkable alignment of the variables that represent different dimensions of variation, namely, the linguistic distance between the predicates, the conceptual distance between the events they represent, the distance between the speaker and the Permitter and Permittee on the animacy/entrenchment/empathy hierarchy, the social and communicative distance between the interlocutors, as well as the strength of collostructional attraction between the constructions and second verb slot fillers. The paper offers several possible explanations for this alignment from a cognitive, functional and historical perspective.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
24
Top 10%
Average
Average
Green
hybrid