Media Publicity and the Voluntary Sector: The Case of Nonprofit Organizations in New York City

Media publicity is an important resource for contemporary voluntary associations, but very little is actually known about the resources and organizational characteristics that are most important for getting media attention. To address this question, we collected and analyzed data on the organizational attributes and news publicity of 739 nonprofit organizations in New York City. We find that an organization's income, paid staff, membership size, and library resources are significantly related to getting media publicity, whereas the number of chapter affiliations is inversely related to publicity. Association type is also a significant factor that influences an organization's ability to get publicity. We discuss the implications that these findings have for current debates about advocacy and civic engagement in the nonprofit sector.     

Luxembourg's Foreign Policy: Does Small Size Help or Hinder?

As a very small state, Luxembourg would not appear to qualify as an influential foreign policy actor either in Europe or the world. Yet the country's international influence has long belied its reputation as a small state. This article reviews the literature on small-state foreign policy and finds that it offers numerous contradictory conclusions. As a case study it then addresses Luxembourg in the 1990s, with particular attention to two themes: its active participation in the European Union (EU) and its policies directed at maintaining economic prosperity. The paper goes on to explain Luxembourgish foreign policy behaviour through three levels of analysis and assesses to what extent smallness is a benefit or a hindrance to Luxembourg's success in meeting its international goals.     

Dairy Farm Survival in a Metropolitan Area: Dutchess County,New York, 1984–19901

Abstract The effect of nonfarm development on dairy farm survival in New York's highly developed lower Hudson Valley is analyzed. The major hypothesis is that significant losses of dairy farms during this period are the result of urban development pressures. Contrary to this hypothesis, however, the bivariate and multivariate analyses of data from a mail survey of all commercial dairy operators in Dutchess County as of 1984 and industry survivors, exiters, and new entrants through 1990 demonstrate that nonfarm development has a negligible effect on dairy farm survival; rather, survival is significantly affected by age of operator and by family disruption. Thus, demographic and family process factors are found to be more powerful determinants of dairy farm survival in this particular metropolitan context.     

The arithmetic of growth: Methods of calculation

This is a tutorial on the relations between population data and the rates of growth that are calculated from the data. For the calculation of rates of growth, discrete and continuous compounding will be compared so that the reader can see the reasons for using the mathematics of continuous compounding, which is the mathematics of exponential growth. Some properties of exponential growth are developed. Semi-logarithmic graphs will be discussed as a device for representing the size of growing populations and for analyzing the nature of the growth. Illustrative examples will be worked out in order to emphasize applications and utility.     

Multiparameter evidence synthesis in epidemiology and medical decision-making: current approaches

Summary.  Alongside the development of meta-analysis as a tool for summarizing research literature, there is renewed interest in broader forms of quantitative synthesis that are aimed at combining evidence from different study designs or evidence on multiple parameters. These have been proposed under various headings: the confidence profile method, cross-design synthesis, hierarchical models and generalized evidence synthesis. Models that are used in health technology assessment are also referred to as representing a synthesis of evidence in a mathematical structure. Here we review alternative approaches to statistical evidence synthesis, and their implications for epidemiology and medical decision-making. The methods include hierarchical models, models informed by evidence on different functions of several parameters and models incorporating both of these features. The need to check for consistency of evidence when using these powerful methods is emphasized. We develop a rationale for evidence synthesis that is based on Bayesian decision modelling and expected value of information theory, which stresses not only the need for a lack of bias in estimates of treatment effects but also a lack of bias in assessments of uncertainty. The increasing reliance of governmental bodies like the UK National Institute for Clinical Excellence on complex evidence synthesis in decision modelling is discussed.