Suchergebnisse

"After breaking free from the Bolsheviks in 1918, Estonia enjoyed independence until 1940, when the country was subsumed by the Soviet Union. Not until 1991 was Estonia able to make its next successful bid for sovereignty. In this book, Rein Taagepera traces the evolution of Estonia from prehistory to the present, when a radical turn of events in the former Soviet Union once again altered the destiny of this Baltic nation.The author explores in depth the remarkable changes in Estonia since 1980, framing his analysis within the larger picture of the Soviet Union and its demise. He also examines the issue of ethnic tensions between Estonians and Russian colonists and speculates on how unrest will affect the future of the country. Throughout his analysis, the author weaves in such key questions as: Why did Sovietization fail? How did Estonia's quest for autonomy affect Soviet dissolution? What role will the country play on the global stage? What will Estonia's future hold?"--Provided by publisher.

For a given electoral system, what average number and sizes of parties and government duration can we expect? Predicting Party Sizes is the first book to make specific predictions that agree with world averages. The basic factors are the numbers of seats in the assembly and in the average electoral district. While previous models tell us only the direction in which to change the electoral system, the present ones also tell us by how much they must be changed so as to obtainthe desired change in average number of parties and cabinet duration. Hence, combined with known particularities of a coun

"This volume presents a comprehensive survey of the peoples who speak Finno-Ugric Languages and have titular republics or autonomous regions within the post-Soviet Russian Federation. Information on the subject was screened by Soviet censors, and only recently have alternative sources of information become available." "The principal threat facing the Finno-Ugrians in the post-Soviet World is as much environmental as political. Communism has gone, but exploitation of natural resources threatens the region's ecology, while the tendency of latter-day rulers in the Kremlin seems to be to continue their predecessors' oppressive policies towards the Finno-Ugrians. The book has been written with a deep commitment to the threatened human and political rights of these endangered peoples."--Jacket

Science walks on two legs. One leg consists of asking: How things are? This leads to observation, measurement, graphing, and statistical description. The other leg consists of asking: How things should be, on logical grounds? This leads to logical models that should become quantitatively predictive. Science largely consists of such models, tested with data. Developed science establishes not only connections among individual factors but also connections among these connections. As an illustration, I use laws about human activity I have found. But social sciences often take the lazy road of fitting raw data with a straight line or some fashionable format, unaware of the need to think and build models based on logic, as stressed by Karl Deutsch. As expounded in my Making Social Sciences More Scientific (2008) and Logical Models and Basic Numeracy in Social Sciences, www.psych.ut.ee/stk/Beginners_Logical_Models.pdf , I call for a major widening in social science methodology.

In any data analysis we should look for ability to predict and for connections to a broader comparative context. Our equations must not predict absurdities, even under extreme circumstances, if we want to be taken seriously as scientists. Poorly done linear regression analysis often does lead to absurd predictions. Fixed exponent and exponential patterns seem more prevalent in social nature than linear patterns. Before applying regression to two variables, graph them against each other, showing the borders of the conceptually allowed space and possible logical anchor points. Transform the data until anchor points and data points do fit a straight line which does not pierce conceptual ceilings or floors. During regression, consider symmetric regression, because Ordinary Least Squares y-on-x and x-on-y differ from each other and their slopes depend on the degree of scatter. After regression, look at the numerical values of parameters and ask what they tell us in a comparative context. When considering multivariable regression, pay more than lip service to Occam's Razor. Adapted from the source document.