Ian Burton
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Ian Burton has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Elena Leuschner
University of Munster
1 shared publication - Rita Sevastjanova
ETH Zurich, Zurich, Switzerland
1 shared publication - Steffen Eckhard
Leibniz-Universität Hannover
1 shared publication - Tilman Kerl
Faculty of Informatics TU Wien
1 shared publication - Vytautas Jankauskas
Zeppelin University, Friedrichshafen, Germany
1 shared publication
Latest Publications
- Open Access
- 6 May 2023
- Published by Springer Nature in The Review of International Organizations
- Vol. 18 (4) , 753-776
The performance of international organizations: a new measure and dataset based on computational text analysis of evaluation reports
International organizations (IOs) of the United Nations (UN) system publish around 750 evaluation reports per year, offering insights on their performance across project, program, institutional, and thematic activities. So far, it was not feasible to extract quantitative performance measures from these text-based reports. Using deep learning, this article presents a novel text-based performance metric: We classify individual sentences as containing a negative, positive, or neutral assessment of the evaluated IO activity and then compute the share of positive sentences per report. Content validation yields that the measure adequately reflects the underlying concept of performance; convergent validation finds high correlation with human-provided performance scores by the World Bank; and construct validation shows that our measure has theoretically expected results. Based on this, we present a novel dataset with performance measures for 1,082 evaluated activities implemented by nine UN system IOs and discuss avenues for further research.
- 6 May 2023
- Published by Springer Nature in The Review of International Organizations
- Vol. 18 (4) , 753-776
Open AccessThe performance of international organizations: a new measure and dataset based on computational text analysis of evaluation reports
International organizations (IOs) of the United Nations (UN) system publish around 750 evaluation reports per year, offering insights on their performance across project, program, institutional, and thematic activities. So far, it was not feasible to extract quantitative performance measures from these text-based reports. Using deep learning, this article presents a novel text-based performance metric: We classify individual sentences as containing a negative, positive, or neutral assessment of the evaluated IO activity and then compute the share of positive sentences per report. Content validation yields that the measure adequately reflects the underlying concept of performance; convergent validation finds high correlation with human-provided performance scores by the World Bank; and construct validation shows that our measure has theoretically expected results. Based on this, we present a novel dataset with performance measures for 1,082 evaluated activities implemented by nine UN system IOs and discuss avenues for further research.