JOURNAL ARTICLE

Gossip-Based Intelligence and Organisational Fragmentation: The Case of the SPLM/A, 1983-2005 - Insider Reflections on Rebel Military Intelligence in South Sudan".

  • Published In: African Journal of Terrorism & Insurgency Research (AJoTIR), 2025, v. 6, n. 2. P. 25 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Dor, Malual Ayom 3 of 3

Abstract

This article examines the role of gossip-based intelligence within the Sudan People's Liberation Movement/Army (SPLM/A) from 1983 to 2005, highlighting its detrimental effects on organizational cohesion and decision-making. Drawing on the author's four decades of insider experience and scholarly analysis, the study reveals how reliance on unverified, rumor-driven intelligence led to wrongful arrests, internal purges, and factionalism, culminating in the SPLM/A's fragmentation in 1991. The research situates these dynamics within institutional intelligence and political conspiracy theories, emphasizing the politicization of intelligence and the absence of effective oversight mechanisms. It further traces the historical and structural influences on SPLM/A intelligence practices, including training under Ethiopian and Sudanese intelligence services, which contributed to a culture of suspicion and internal targeting. The article concludes by recommending strengthened accountability, improved training, and transparent recruitment to mitigate the harmful impact of gossip in intelligence operations.

Additional Information

  • Source:African Journal of Terrorism & Insurgency Research (AJoTIR). 2025/12, Vol. 6, Issue 2, p25
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2025
  • ISSN:2732-4990
  • DOI:10.31920/2732-5008/2025/v6n2a2
  • Accession Number:189751757
  • Copyright Statement:Copyright of African Journal of Terrorism & Insurgency Research (AJoTIR) is the property of Adonis & Abbey Publishers Ltd. 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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