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Introducing the PeaceKeeping Operations Corpus (PKOC)

Author

Listed:
  • Elio Amicarelli

    (Independent Researcher)

  • Jessica Di Salvatore

    (Department of Politics and International Studies, 2707University of Warwick)

Abstract

Scholars have used United Nations Secretary-General’s (UNSG) reports to extract information on peacekeeping operations (PKOs). As key peacekeeping political documents, UNSG reports contain much more information on the politics of peacekeeping. Furthermore, manually extracting information is costly and time-consuming. By providing a machine-readable collection of the UN Secretary-General’s Reports on PKOs (1994–2020), the PeaceKeeping Operations Corpus (PKOC) offers highly structured and multiformat text data that connect the peace and conflict research community to recent advancements in text-as-data techniques. Besides paving the way for the first quantitative content analyses on PKOs, PKOC speeds up and expands the range of information analysable from these documents and allows researchers to query them in a quicker, systematic and reproducible way. In this article, we discuss PKOC’s core characteristics. As illustration of the innovative potential of PKOC, we show how text-as-data approaches provide more nuanced understanding on PKOs’ evolution toward multidimensionality, both over time and within missions. While last generation PKOs are assumed to be multidimensional, we show how they vary in multidimensionality and how their complexity also changes throughout their life-cycle.

Suggested Citation

  • Elio Amicarelli & Jessica Di Salvatore, 2021. "Introducing the PeaceKeeping Operations Corpus (PKOC)," Journal of Peace Research, Peace Research Institute Oslo, vol. 58(5), pages 1137-1148, September.
    • Handle: RePEc:sae:joupea:v:58:y:2021:i:5:p:1137-1148
    DOI: 10.1177/0022343320978693
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    References listed on IDEAS

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    1. Benoit, Kenneth & Conway, Drew & Lauderdale, Benjamin E. & Laver, Michael & Mikhaylov, Slava, 2016. "Crowd-sourced Text Analysis: Reproducible and Agile Production of Political Data," American Political Science Review, Cambridge University Press, vol. 110(2), pages 278-295, May.
    2. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    3. Munger, Kevin & Bonneau, Richard & Nagler, Jonathan & Tucker, Joshua A., 2019. "Elites Tweet to Get Feet Off the Streets: Measuring Regime Social Media Strategies During Protest," Political Science Research and Methods, Cambridge University Press, vol. 7(4), pages 815-834, October.
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