Examining likelihoods in 2012: autocratic & democratic regime change

Events throughout 2011 have demonstrated that terrorism remains a high priority for nation-states through the world. Regime and nation-state stability have played an influential role, particularity in the Middle East and Africa in affecting global terrorism. In an attempt to map out what 2012 may bring, examining regime political stability is a useful starting point for a look at global terrorism in 2012. Here Jay Ulfelder provides an engaging and thought-provoking examination into the likelihood of transitions in 2012 of autocracies into democracies

International Political Science Review Contentious Collective Action and the Breakdown of Authoritarian Regimes Contentious Collective Action and the Breakdown of Authoritarian Regimes

ABSTRACT. Prior research indicates that different types of authoritarian regimes break down in different ways because key cadres in those regimes have different interests and face different strategic environments. Building on that theoretical foundation, I use event history models to examine the effects of contentious collective action on the likelihood of authoritarian breakdown. This analysis shows that some kinds of autocracy are more vulnerable to breakdown in the wake of contentious events than others, and that the strength and direction of this effect varies not only across types of authoritarianism, but across forms of collective action as well

Democracy/Autocracy Data Set

Event history data (a.k.a. survival data) on episodes of democracy and autocracy in countries worldwide during the period 1955-2010. In time-series cross-sectional format for discrete-time survival analysis with country-years as the unit of observation

Mining texts to efficiently generate global data on political regime types

We describe the design and results of an experiment in using text-mining and machine-learning techniques to generate annual measures of national political regime types. Valid and reliable measures of countries’ forms of national government are essential to cross-national and dynamic analysis of many phenomena of great interest to political scientists, including civil war, interstate war, democratization, and coups d’état. Unfortunately, traditional measures of regime type are very expensive to produce, and observations for ambiguous cases are often sharply contested. In this project, we train a series of support vector machine (SVM) classifiers to infer regime type from textual data sources. To train the classifiers, we used vectorized textual reports from Freedom House and the State Department as features for a training set of prelabeled regime type data. To validate our SVM classifiers, we compare their predictions in an out-of-sample context, and the performance results across a variety of metrics (accuracy, precision, recall) are very high. The results of this project highlight the ability of these techniques to contribute to producing real-time data sources for use in political science that can also be routinely updated at much lower cost than human-coded data. To this end, we set up a text-processing pipeline that pulls updated textual data from selected sources, conducts feature extraction, and applies supervised machine learning methods to produce measures of regime type. This pipeline, written in Python, can be pulled from the Github repository associated with this project and easily extended as more data becomes available