世界経済・社会統計 = World development indicators

Ethnic Groups in Conflict

The Tragedy of Great Power Politics

Why Men Rebel was first published in 1970 on the heels of a decade of political violence and protest not only in remote corners of Africa and Southeast Asia, but also at home in the United States. Forty years later, the world is riveted on uprisings in the Middle East, and the United States has been overtaken by a focus on international terrorism and a fascination with citizen movements at home and abroad. Do the arguments of 1970 apply today? Why Men Rebel lends new insight into contemporary challenges of transnational recruitment and organization, multimedia mobilization, and terrorism.

Why Men Rebel

Supervised learning techniques construct predictive models by learning from a large number of training examples, where each training example has a label indicating its ground-truth output. Though current techniques have achieved great success, it is noteworthy that in many tasks it is difficult to get strong supervision information like fully ground-truth labels due to the high cost of the data-labeling process. Thus, it is desirable for machine-learning techniques to work with weak supervision. This article reviews some research progress of weakly supervised learning, focusing on three typical types of weak supervision: incomplete supervision, where only a subset of training data is given with labels; inexact supervision, where the training data are given with only coarse-grained labels; and inaccurate supervision, where the given labels are not always ground-truth.

/pdf/a-brief-introduction-to-weakly-supervised-learning-5cnab1ys70.pdf

A brief introduction to weakly supervised learning

Contemporary research on civil war has largely dismissed the role of political and economic grievances, focusing instead on opportunities for conflict. However, these strong claims rest on questionable theoretical and empirical grounds. Whereas scholars have examined primarily the relationship between individual inequality and conflict, we argue that horizontal inequalities between politically relevant ethnic groups and states at large can promote ethnonationalist conflict. Extending the empirical scope to the entire world, this article introduces a new spatial method that combines our newly geocoded data on ethnic groups’ settlement areas with spatial wealth estimates. Based on these methodological advances, we find that, in highly unequal societies, both rich and poor groups fight more often than those groups whose wealth lies closer to the country average. Our results remain robust to a number of alternative sample definitions and specifications.

/pdf/horizontal-inequalities-and-ethnonationalist-civil-war-a-1eupgm5fiu.pdf

Horizontal Inequalities and Ethnonationalist Civil War: A Global Comparison

We describe CShapes, a new dataset that provides historical maps of state boundaries and capitals in the post-World War II period. The dataset is coded according to both the Correlates of War and the Gleditsch and Ward (1999) state lists, and is therefore compatible with a great number of existing databases in the discipline. Provided in a geographic data format, CShapes can be used directly with standard GIS software, allowing a wide range of spatial computations. In addition, we supply a CShapes package for the R statistical toolkit. This package enables researchers without GIS skills to perform various useful operations on the GIS maps. The paper introduces the CShapes dataset and structure and gives three examples of how to use CShapes in political science research. First, we show how results from quantitative analysis can be depicted intuitively as a map. The second application gives an example of computing indicators on the CShapes maps, which can then be used in statistical tests. Third, we illustr...

https://www.tandfonline.com/doi/pdf/10.1080/03050620903554614

The Geography of the International System: The CShapes Dataset

Whether qualitative or quantitative, contemporary civil-war studies have a tendency to over-aggregate empirical evidence. In order to open the black box of the state, it is necessary to pinpoint th...

Representing Ethnic Groups in Space: A New Dataset

This article introduces GeoEPR, a geocoded version of the Ethnic Power Relations (EPR) dataset that charts politically relevant ethnic groups across space and time. We describe the dataset in detai...

/pdf/politically-relevant-ethnic-groups-across-space-and-time-44ay2l96ak.pdf

Politically Relevant Ethnic Groups across Space and Time : Introducing the GeoEPR Dataset

Recent data collections about political violence are frequently based on media reports, which can lead to reporting bias. This is an issue in particular for the emergent literature on communication technology and conflict, since this technology may not only affect violence, but also the reporting about it. Using the effect of cellphones on violence as an example, this article presents a quantitative assessment of reporting bias in a micro-level analysis. Comparing media-based event reports and those from military sources, the results show that the purported violence-increasing effect of cellphone coverage is partly due to higher reporting rates of violence in cellphone-covered areas. A simple diagnostic procedure for this problem is implemented. Applied to the analysis of cellphones and violence in Africa, it produces a pattern that is consistent with reporting bias driving much of the effect found in the Pierskalla and Hollenbach (2013) study about this topic.

Nils B. Weidmann

Papers

Horizontal Inequalities and Ethnonationalist Civil War: A Global Comparison

The Geography of the International System: The CShapes Dataset

Representing Ethnic Groups in Space: A New Dataset

Politically Relevant Ethnic Groups across Space and Time : Introducing the GeoEPR Dataset

A Closer Look at Reporting Bias in Conflict Event Data