JOURNAL ARTICLE

The Use of ‹Collective Memory› in Muḥammad alĠazālī’s Religious Discourse – the Battle of Badr (624) and the October War (1973).

  • Published In: Schweizerische Zeitschrift für Religions & Kulturgeschichte, 2024, v. 118. P. 125 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Badini, Francesca 3 of 3

Abstract

This article aims to explore the possibility of analyzing the concept of retrospective utopia in Muḥammad al-Ġazālī’s (1917–1996) predication as a reconstruction of ‹collective memory›. The analysis considers the sermon that al-Ġazālī presented at the ʿAmr ibn al-ʿĀṣ mosque in Cairo on 14 December 14, 1973, concerning verse Q. 2:217. The analysis of the sermon under consideration presents the exegetical strategy with which al-Ġazālī managed to justify the events related to the October War of 1973, i.e. the attack by Egyptian forces during what was considered the holy month, at the Battle of Badr in 624, an event reworked by Islamic tradition and presented to the Islamic community from the perspective of ‹collective memory›. In this contribution, I present the two historical events considered, contextualize al-Ġazālī’s preaching activity in 1973, hinting at the relationship between the exegete and the political representation of the Egyptian state in those years, and then analyze the sermon considered, explaining why the author chose to justify the actions of President Anwar Sadat (1918–1981) through religious proselytism and the recall of ‹collective memory›. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Schweizerische Zeitschrift für Religions & Kulturgeschichte. 2024/01, Vol. 118, p125
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2024
  • ISSN:1661-3880
  • DOI:10.24894/2673-3641.00167
  • Accession Number:187771244
  • Copyright Statement:Copyright of Schweizerische Zeitschrift für Religions & Kulturgeschichte is the property of Schwabe Verlag 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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