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Lookup NU author(s): Dr Daniel DuncanORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
The English alternative embedded passive (AEP), or ‘needs washed construction,’ is a noncanonical morphosyntactic feature found in some American and British Englishes. It involves a matrix verb surfacing immediately before a participle. Previous research has described this construction as only licit with matrix need, want, and like; however, isolated examples of the AEP with additional matrix verbs have surfaced. These rarely attested instances raise questions regarding the basic description of the construction and how matrix verb availability is constrained, as well as whether the AEP is truly the same feature across AmE and BrE varieties. This paper utilizes a large-scale grammaticality judgement survey to obtain as exhaustive a set of AEP matrix verbs as possible. Results show that far more verbs can be used in the AEP than previously attested. Acceptance is constrained by lexical semantics, verbal syntax, and verb productivity. This alternative view of the AEP as a more generalized phenomenon nevertheless shows a strong link between AmE and BrE varieties, as the constraints are nearly identical across the nations. The findings illustrate how attention to rarely attested or non-attested data can inform morphosyntactic and dialectological research.
Author(s): Duncan D
Publication type: Article
Publication status: Published
Journal: Linguistics
Year: 2024
Pages: epub ahead of print
Online publication date: 25/03/2024
Acceptance date: 20/02/2024
Date deposited: 19/02/2024
ISSN (print): 0024-3949
ISSN (electronic): 1613-396X
Publisher: De Gruyter Mouton
URL: https://doi.org/10.1515/ling-2023-0170
DOI: 10.1515/ling-2023-0170
Data Access Statement: Materials associated with this study can be found at the public OSF repository https://osf.io/75buw/. These include materials and R code for the construction of the bipartite network used to obtain language-internal factors; experimental design and stimuli; raw survey results; cleaned survey results and R code for regression analysis; and cleaned survey results, R code, and zip code/postcode shapefiles for geospatial analysis.
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