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ISSN Approved Journal || eISSN: 2582-8185 || CODEN: IJSRO2 || Impact Factor 8.2 || Google Scholar and CrossRef Indexed

Peer Reviewed and Referred Journal || Free Certificate of Publication

Research and review articles are invited for publication in March 2026 (Volume 18, Issue 3) Submit manuscript

Recovering Lost Lives: Machine Learning to Surface African Women in Trans-Atlantic Slave Records

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  • Recovering Lost Lives: Machine Learning to Surface African Women in Trans-Atlantic Slave Records

Clement Tetteh *

Department of History, University of Ghana.

Review Article

 

International Journal of Science and Research Archive, 2022, 07(02), 899-911.
Article DOI: 10.30574/ijsra.2022.7.2.0314
DOI url: https://doi.org/10.30574/ijsra.2022.7.2.0314

Received on 07 October 2022; revised on 19 November 2022; accepted on 28 November 2022

The historical record of the trans-Atlantic slave trade remains profoundly incomplete, particularly regarding the lives of African women whose identities were disproportionately erased, anonymized, or collapsed into generic labor categories. Existing archival collections including ship manifests, plantation inventories, baptismal rolls, court testimonies, sale ledgers, and manumission documents encode gender, kinship, ethnicity, and geographic origin unevenly due to colonial recordkeeping practices. As a result, women’s labor roles, reproductive histories, and social networks often disappear into ambiguous descriptors such as “girl,” “wench,” “daughter,” or unnamed household dependents. This research project proposes a machine learning framework to recover African women’s identities and reconstruct relational networks across fragmented documentary sources. The method integrates optical character recognition (OCR) to digitize handwritten and degraded archival documents; natural language processing (NLP) models trained to detect gender-coded vocabulary, kinship relational markers, and African naming patterns; and probabilistic entity resolution to match individuals across dispersed archival collections. Rather than displacing human interpretation, the approach is designed to augment historical reasoning, acting as a recovery tool that flags overlooked individuals and generates new research leads. Collaborative integration with existing digital humanities infrastructures especially Slave Voyages and Freedom on the Move enables scale, interoperability, and standardized metadata exchange. This project contributes to ongoing efforts in reparative archival work, feminist historiography, and Black Atlantic studies by systematically addressing archival silences. By leveraging computational approaches to illuminate erased presences, it advances a historically grounded, ethically sensitive framework for restoring African women to the narrative of Atlantic world history.

Machine learning; Archival recovery; African diaspora; Trans-Atlantic slavery; Gender analysis; Digital humanities

https://ijsra.net/sites/default/files/fulltext_pdf/IJSRA-2022-0314.pdf

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Clement Tetteh. Recovering Lost Lives: Machine Learning to Surface African Women in Trans-Atlantic Slave Records. International Journal of Science and Research Archive, 2022, 07(02), 899-911. Article DOI: https://doi.org/10.30574/ijsra.2022.7.2.0314

Copyright © Author(s). All rights reserved. This article is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and source, a link to the license is provided, and any changes made are indicated.


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