JOURNAL ARTICLE
Aquinas' Theory of Love and the Depiction of Love in Popular and High Culture.
Published In: International Journal of Literary Humanities, 2024, v. 22, n. 1. P. 1 1 of 3
Database: Humanities Source Ultimate 2 of 3
Authored By: Green, Michael K. 3 of 3
Abstract
The literary and musical arts provide a wealth of descriptions, expressions, and simulations of love. Philosophy provides abstract analyses of love. Humanistic verification uses the depictions of love in the literary arts to test the validity of the philosophical analyses. This article explores this process by relating Thomas Aquinas' account of love to popular culture in the form of the lyrics of rock songs of the 1960s and to high culture in the form of William Shakespeare's "All's Well That Ends Well," "Antony and Cleopatra," "Romeo and Juliet," and "The Taming of the Shrew." More precisely, in his "Summa Theologiae," Aquinas distinguishes eight aspects of love--melting, commonalities, bonding, ecstasis, zeal, joy, sorrow, and fervor. If these are indeed aspects of love, then descriptions and/or expressions of them should be common in popular and high culture. The result of this humanistic verification in this case is that Aquinas' eight characteristics are common in both popular and high culture. However, his account is incomplete because it fails to see that anger, jealousy, and gratitude are also common attributes of love. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Literary Humanities. 2024/03, Vol. 22, Issue 1, p1
- Document Type:Article
- Subject Area:Literature and Writing
- Publication Date:2024
- ISSN:23277912
- DOI:10.18848/2327-7912/CGP/v22i01/1-21
- Accession Number:175889311
- Copyright Statement:Copyright of International Journal of Literary Humanities is the property of Common Ground Research Networks 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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