JOURNAL ARTICLE
Semiotics of a Covid landscape: Tactical urbanism in a pandemic.
Published In: Linguistic Landscape: An International Journal (LL), 2023, v. 9, n. 3. P. 226 1 of 3
Database: Communication Source 2 of 3
Authored By: Modan, Gabriella; Schaller, Susanna 3 of 3
Abstract
This paper brings together urban planning and linguistic perspectives to examine the semiotic landscape of a Washington, DC 'streatery' in the context of the intersecting public health- and place-based economic crises unleashed by the Covid-19 pandemic. Drawing from Garay-Huamán and Irazábal-Zurita's (2021) work on neoliberal Social Structures of Accumulation (SSA), we examine how different layers of Adams Morgan's emergent Covid landscape are rooted in the dynamics of capitalist accumulation through urban placemaking strategies. We focus on signs put up by the Business Improvement District (BID) that explain the public health regulations applicable to the area through discourse that playfully encourages people to social distance and wear masks. These signs utilize three linguistic or semiotic discourses: hygiene, humor and play, and anti-Trump politics. The signs serve as a bona fide effort to both halt the spread of the coronavirus and take a political stance. At the same time as the signs promote public health, their commodified aestheticization of hygiene and politics also serves commercial interests. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Linguistic Landscape: An International Journal (LL). 2023/09, Vol. 9, Issue 3, p226
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2023
- ISSN:2214-9953
- DOI:10.1075/ll.22038.mod
- Accession Number:172004682
- Copyright Statement:Copyright of Linguistic Landscape: An International Journal (LL) is the property of John Benjamins Publishing Co. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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