University Library, University of Illinois at Urbana-Champaign
Social Political Economic Event Dataset (SPEED): Liberia, Philippines, and Sierra Leone (1979-2008). Cline Center for Advanced Social Research. V1.0.0. August 29
Nardulli, Peter; Peyton, Buddy; Bajjalieh, Joseph; Singh, Ajay; Martin, Michael; Shalmon, Dan; Althaus, Scott (2019): Social Political Economic Event Dataset (SPEED): Liberia, Philippines, and Sierra Leone (1979-2008). Cline Center for Advanced Social Research. V1.0.0. August 29. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-7407320_V1
Rhodes, A.,Waleij, A., Goran, W., Singh, A. and Nardulli, P. (2011). Proactive peacebuilding with natural resource assets (ERDC/CERL TR-11). Champaign: US Army Engineer Research and Development Center-Construction Engineering Research Laboratory. Unpublished manuscript
This is part of the Cline Center’s ongoing Social, Political and Economic Event Database Project (SPEED) project. Each observation represents an event involving civil unrest, repression, or political violence in Sierra Leone, Liberia, and the Philippines (1979-2009). These data were produced in an effort to describe the relationship between exploitation of natural resources and civil conflict, and to identify policy interventions that might address resource-related grievances and mitigate civil strife.
This work is the result of a collaboration between the US Army Corps of Engineers’ Construction Engineer Research Laboratory (ERDC-CERL), the Swedish Defence Research Agency (FOI) and the Cline Center for Advanced Social Research (CCASR). The project team selected case studies focused on nations with a long history of civil conflict, as well as lucrative natural resources.
The Cline Center extracted these events from country-specific articles published in English by the British Broadcasting Corporation (BBC) Summary of World Broadcasts (SWB) from 1979-2008 and the CIA’s Foreign Broadcast Information Service (FBIS) 1999-2004. Articles were selected if they mentioned a country of interest, and were tagged as relevant by a Cline Center-built machine learning-based classification algorithm. Trained analysts extracted nearly 10,000 events from nearly 5,000 documents. The codebook—available in PDF form below—describes the data and production process in greater detail.
Cline Center for Advanced Social Research; civil unrest; Social Political Economic Event Dataset (SPEED); political; event data; war; conflict; protest; violence; social; SPEED; Cline Center; Political Science