Towards more confidence: about the roles of social scientists in participatory policy making

From June 26 to 27, the workshop “Ironists, Reformers, or Rebels? The Role of the Social Sciences in Participatory Policy Making” took place at the Collegium Helveticum of the UZH/ETH in Zurich. The organisers’ motivation was the apparently missing involvement of social scientists in public engagement processes. This impression persists because, while social scientists often observe public debates or develop participatory methods for public policy-making, they rarely take part in those processes themselves. A closer look at ethics commissions, expert committees or public hearings concerned with science and technology issues shows natural scientists, physicians, lawyers and the occasional philosopher. Sociologists, anthropologists and other social scientists, on the other hand, are often not involved. Because of this imbalance, the organisers’ aim was to bring together scholars and researchers from different areas of the social sciences to consider the role of their disciplines in public policy making. This article will focus on some of the ideas about specific roles of social scientists in participatory policy-making, discussed at the workshop, and their implications and give a commentary on some future prospects of the social sciences.     

Is Mothers’ Work Related to Childhood Weight Changes in the United States?

Taking family structure, father’s work intensity, and children’s developmental stages into consideration, this study examined the effect of a mother’s previous and contemporaneous work (employment and weekly work hours) on their children’s weight and their likelihood of having weight problems such as obesity, overweight, and underweight. Using data from the Early Childhood Longitudinal Study-Kindergarten Class of 1998–1999 (ECLS-K), this study adopted a fixed effect model and found that for 3rd to 5th graders, an increase in mothers’ previous work hours among two-parent families below the federal poverty level (FPL) increased the risk of the child being obese, while the current employment of single mothers below the FPL reduced the risk of the child being obese. Surprisingly, the employment of single mothers below the FPL increased the risk of the child being underweight, especially for 1st to 3rd graders.     

Embedded expertise: a conceptual framework for reconstructing knowledge orders,their transformation and local specificities

The role of science in modern democracies has long been a topic of heated debate. Recent scholarship has increasingly focused on the social construction of the science–policy–politics nexus. For understanding both the local specificity of concrete configurations and general trends much is to be gained from an analytical framework. In a first introductory section we describe already existing categorizations and typologies, identifying a research gap: a framework for reconstructing the discursive forces shaping the science–policy–politics interface based on both mutually exclusive and exhaustive categories. Our answer is the concept of knowledge orders. Building on already existing scholarship in this field, we define knowledge orders as discursive orders structuring the production of expertise. We distinguish between three levels of knowledge orders: the generation and communication of science-based expertise, the regulation of these processes, and reflexive discourses in which the epistemic authority and the political relevance of science-based expertise are (de)legitimized and renegotiated and the regulation of the science–policy–politics nexus debated. Each level can be analyzed in terms of the criteria based on which competence is attributed (social dimension) and at when political or scientific criteria of relevance and validity are to be applied (procedural dimension). The framework is illustrated by referring to both well-known examples in science studies and our own research on science–policy–politics arrangements in the field of food safety and employment policy.     

An Empirical Analysis on Determinants of Aid Allocation by South Korean Civil Society Organizations

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - This paper examines whether three sets of factors—humanitarianism, the South Korean government’s official aid,...     

Interval Estimation for the Correlation Coefficient

ABSTRACT

The correlation coefficient (CC) is a standard measure of a possible linear association between two continuous random variables. The CC plays a significant role in many scientific disciplines. For a bivariate normal distribution, there are many types of confidence intervals for the CC, such as z-transformation and maximum likelihood-based intervals. However, when the underlying bivariate distribution is unknown, the construction of confidence intervals for the CC is not well-developed. In this paper, we discuss various interval estimation methods for the CC. We propose a generalized confidence interval for the CC when the underlying bivariate distribution is a normal distribution, and two empirical likelihood-based intervals for the CC when the underlying bivariate distribution is unknown. We also conduct extensive simulation studies to compare the new intervals with existing intervals in terms of coverage probability and interval length. Finally, two real examples are used to demonstrate the application of the proposed methods.     

Review: Reversed low-rank ANOVA model for transforming high dimensional genetic data into low dimension

A general modeling procedure for analyzing genetic data is reviewed. We review ANOVA type model that can handle both the continuous and discrete genetic variables in one modeling framework. Unlike the regression type models which typically set the phenotype variable as a response, this ANOVA model treats the phenotype variable as an explanatory variable. By reversely treating the phenotype variable, usual high dimensional problem is turned into low dimension. Instead, the ANOVA model always includes interaction term between the genetic locations and phenotype variable to find potential association between them. The interaction term is designed to be low rank with the multiplication of bilinear terms so that the required number of parameters is kept in a manageable degree. We compare the performance of the reviewed ANOVA model to the other popular methods via microarray and SNP data sets.     

A regression approach for estimating the parameters of the covariance function of a stationary spatial random process

We consider the problem of estimating the parameters of the covariance function of a stationary spatial random process. In spatial statistics, there are widely used parametric forms for the covariance functions, and various methods for estimating the parameters have been proposed in the literature. We develop a method for estimating the parameters of the covariance function that is based on a regression approach. Our method utilizes pairs of observations whose distances are closest to a value h>0$h>0$ which is chosen in a way that the estimated correlation at distance h is a predetermined value. We demonstrate the effectiveness of our procedure by simulation studies and an application to a water pH data set. Simulation studies show that our method outperforms all well-known least squares-based approaches to the variogram estimation and is comparable to the maximum likelihood estimation of the parameters of the covariance function. We also show that under a mixing condition on the random field, the proposed estimator is consistent for standard one parameter models for stationary correlation functions.     

Dealing with Endogeneity to Understand the Societal Impact of the Third Sector: Why Should We Care and What Can We Do about It?

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - Endogeneity is often regarded as a key barrier in establishing the causal relationship between the third sector and its...