JOURNAL ARTICLE
Zen Buddhism on the Limits of Knowledge and the Practice of Wisdom.
Published In: Archiv für Begriffsgeschichte, 2025, v. 67, n. 2. P. 85 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Davis, Bret W. 3 of 3
Abstract
This article examines how the Zen Buddhist tradition has conceived of the relation between intellectual knowledge and holistic wisdom. It begins with some reflections on Aristotle and the history of Western philosophy in order to pose the metaphilosophical question of whether, in modern times, philo-sophia has become more narrowly philo-epistēmē. With this critical question in mind, and in order to approach the Zen Buddhist tradition’s very different understanding of the nature of knowledge and the pursuit of wisdom, the article turns to two modern Japanese philosophers of the Kyoto School who were Zen practitioners as well as scholars of Western philosophy, Nishida Kitarō and Nishitani Keiji. The remainder of the article is dedicated to investigating how the Zen tradition—by drawing on and developing Mahāyāna Buddhist (and also Daoist) texts, teachings, and practices—has sought to reveal not only the limits of knowledge but also the delimiting effects of acts of knowing. Moreover, it discusses how Zen meditation and other forms of holistic practice are intended to awaken and cultivate a wisdom that enables one to intuitively understand when, where, and how to employ this or that perspectivally delimited form of knowledge. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Archiv für Begriffsgeschichte. 2025/07, Vol. 67, Issue 2, p85
- Document Type:Article
- Subject Area:Literature and Writing
- Publication Date:2025
- ISSN:0003-8946
- Accession Number:192855077
- Copyright Statement:Copyright of Archiv für Begriffsgeschichte is the property of Felix Meiner Verlag GmbH 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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