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The science of human rights requires valid comparisons of repression levels across time and space. Though extensive data collection efforts have made such comparisons possible in principle, statistical measures based on simple additive scales made them rare in practice. This article uses a dynamic measurement model that contrasts with current approaches by (1) accounting for the fact that human rights indicators vary in the level of information they provide about the latent level of repression, (2) allowing realistic descriptions of measurement uncertainty in the form of credible intervals and (3) providing a theoretical motivation for modeling temporal dependence in human rights levels. It presents several techniques, which demonstrate that the dynamic ordinal item-response theory model outperforms its static counterpart. Adapted from the source document.

The science of human rights requires valid comparisons of repression levels across time and space. Though extensive data collection efforts have made such comparisons possible in principle, statistical measures based on simple additive scales made them rare in practice. This article uses a dynamic measurement model that contrasts with current approaches by (1) accounting for the fact that human rights indicators vary in the level of information they provide about the latent level of repression, (2) allowing realistic descriptions of measurement uncertainty in the form of credible intervals and (3) providing a theoretical motivation for modeling temporal dependence in human rights levels. It presents several techniques, which demonstrate that the dynamic ordinal item-response theory model outperforms its static counterpart.

This study explores the relationships between state violations of different human rights. Though most quantitative studies in international relations treat different types of repressive behaviors as either independent or arising from the same underlying process, significant insights are gained by conceptualizing different human rights violations as separate but dependent processes. We present a theoretical framework for conceptualizing the mechanisms relating human rights practices and produce a novel measurement strategy based on network analysis for exploring these relationships. We illustrate high levels of complementarity between most human rights practices. Substitution effects, in contrast, are occasionally substantial but relatively rare. Finally, using empirically informed Monte Carlo analyses, we present predictions regarding likely sequences of rights violations resulting in extreme violations of different physical integrity rights.

The central purpose of this article is to establish the relationship. - between power projection, technology, and economic power.. - How economically powerful does a state need to be before it can. - afford the capital intensive technologies, foreign bases, and military. - and logistical forces associated with global power projection?. - The specific research question we focus on in this article is: What. - determines how far states send their military forces? We argue. - that as the costs associated with projecting power decrease or as. - the wealth necessary to project power increases, states will project. - power more frequently and at greater distances. We use a system. - level time series analysis from 1870-1936 and a dispute level analysis. - on all militarized international disputes from 1870-2000 to. - test these propositions. This article is the first to demonstrate empirically. - that the distance and frequency of power projection is a. - function of the cost of projecting power. We close with a discussion. - of contemporary states building power projection capabilities. - and how future research might build from our research to explain. - this behavior. (International Interactions (London)/ FUB)

AbstractScholars systematically mismeasure power resources and military burdens by using gross domestic product (GDP) as a proxy for the income states can devote to arming. The core problem is that GDP confounds two conceptually distinct forms of income into one additive indicator. Subsistence income represents resources needed to provide the "bread" necessary to cover the basic subsistence needs of the population. Surplus income represents the remaining resources that could be allocated to "guns" or "butter." Our new measure of surplus domestic product (SDP) corrects for this measurement error by decomposing subsistence income and surplus income from total GDP. Validation exercises demonstrate that SDP outperforms GDP at measuring the distribution of power resources. Though theoretically we expect states' decisions to arm are influenced by the distribution of power; empirical models using GDP find mixed support for this expectation. Strikingly, using SDP reveals strong support for this proposition.

Counting repressive events is difficult because state leaders have an incentive to conceal actions of their subordinates and destroy evidence of abuse. In this article, we extend existing latent variable modeling techniques in the study of repression to account for the uncertainty inherent in count data generated for this type of difficult-to-observe event. We demonstrate the utility of the model by focusing on a dataset that defines 'one-sided-killing' as government-caused deaths of non-combatants. In addition to generating more precise estimates of latent repression levels, the model also estimates the probability that a state engaged in one-sided-killing and the predictive distribution of deaths for each country-year in the dataset. These new event-based, count estimates will be useful for researchers interested in this type of data but skeptical of the comparability of such events across countries and over time. Our modeling framework also provides a principled method for inferring unobserved count variables based on conceptually related categorical information.