JOURNAL ARTICLE

Who owns the data? Fisheries science, epistemic power, and Russia–Africa relations.

  • Published In: New Perspectives: Interdisciplinary Journal of Central & East European Politics & International Relations, 2026, v. 34, n. 1. P. 70 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Bamidele, Seun; Idowu, Olusegun Oladele 3 of 3

Abstract

The article examines fisheries science as a critical site of epistemic power within Russia–Africa relations, focusing on the politics of data ownership and its implications for governance and sovereignty in African coastal states. It argues that control over fisheries data, methodologies, and scientific infrastructure—collectively termed epistemic infrastructure—shapes regulatory authority, access negotiations, and states' capacity to manage marine resources, often reproducing asymmetrical dependencies rooted in historical patterns of knowledge extraction. Through case studies in Namibia, Senegal, Mauritania, and Mozambique, the analysis highlights how Russian fisheries cooperation combines technical capacity-building with enduring external influence over data and decision-making. The article further discusses African efforts to reclaim data sovereignty and integrate local ecological knowledge, situating these within decolonial and environmental justice frameworks that call for equitable, transparent, and locally grounded fisheries governance.

Additional Information

  • Source:New Perspectives: Interdisciplinary Journal of Central & East European Politics & International Relations. 2026/03, Vol. 34, Issue 1, p70
  • Document Type:Article
  • Subject Area:Agriculture and Agribusiness
  • Publication Date:2026
  • ISSN:2336-825X
  • DOI:10.1177/2336825X261416356
  • Accession Number:191572881
  • Copyright Statement:Copyright of New Perspectives: Interdisciplinary Journal of Central & East European Politics & International Relations is the property of Sage Publications Inc. 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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