Lead researchers:  Dr Eduardo Albrecht and Dr Chido Samantha Mutangadura

Partner: United Nations University (UNU)

Duration: April 2023 – March 2024  

Countries: Central African Republic, the Democratic Republic of Congo, Mali, South Sudan, Sudan 

Multilateral interventions through United Nations Peace Support Operations (PSOs) are key international responses to prolonged armed conflict in contexts where interstate conflict spills over borders with neighbouring states. Understanding the effectiveness of PSOs in reducing the spread of conflict across national borders is critical in improving the capacity of states and international actors to tailor targeted response plans and reduce the risk of cross-border violence. Recent years have seen rapid development of new technologies such as machine learning that enable the identification of informative patterns from large amounts of information.  

This study aimed to cover a critical methodological gap by testing the utility of machine learning in identifying potential correlations between United Nations PSO personnel characteristics and cross border stability dynamics. Experiments examined data relative to PSOs and cross border conflict dynamics in five case studies: the Central African Republic, the Democratic Republic of Congo, Mali, South Sudan, and the disputed Abyei region. The research adopted a mixed method approach using a combination of qualitative (interviews and focus group discussion) and quantitative data. Quantitative data relating to PSO personnel characteristics (gender, origin, and status) and conflict location/intensity dynamics was sourced from UN and ACLED data sets covering a period of nine years from April 2014 to April 2023. This quantitative data was analysed through machine learning, while qualitative research helped critically evaluate data representativeness and assess findings. 

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For more information regarding this research, contact [email protected]