JOURNAL ARTICLE
"She is the Great Outside": Ecofeminist Potentiality in H. G. Wells's The Sea Lady.
Published In: Anglia: Journal of English Philology / Zeitschrift für Englische Philologie, 2024, v. 142, n. 1. P. 49 1 of 3
Database: Humanities Source Ultimate 2 of 3
Authored By: Kalthoff, Katharina Andrea 3 of 3
Abstract
H. G. Wells's serial fable The Sea Lady (1902) offers a satirical commentary on the Victorian Age: It draws a societal allegory that criticises both a cohesive conception of the natural world, and Victorian notions of the domestic realm – in the private and national sense of the term. Yet the story has long been regarded as an oddity in the author's oeuvre. Wells's Victorian contemporaries criticised its lack of scientific detail so characteristic of his more successful scientific romances. The reason for this appears to be the story's eponymous central figure – a mermaid, supposedly rather the stuff of legend, folklore and fairy tales. However, by opening up a utopian realm in which 'better dreams' await, the figure of the sea lady destabilises Victorian moral orders as a potential discovery of the future lingers on the horizon of the century's turn. In this article, I foreground the story's ecofeminist potential by analysing the intersections between its unconventional form, the 'untameable female' protagonist at its centre, and the specific oceanic and littoral environments it presents. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Anglia: Journal of English Philology / Zeitschrift für Englische Philologie. 2024/03, Vol. 142, Issue 1, p49
- Document Type:Article
- Subject Area:Literature and Writing
- Publication Date:2024
- ISSN:03405222
- DOI:10.1515/ang-2024-0005
- Accession Number:176844915
- Copyright Statement:Copyright of Anglia: Journal of English Philology / Zeitschrift für Englische Philologie is the property of De Gruyter 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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