Explaining public support for counterproductive homeless policy: the role of disgust

Federal, state, and city governments spend substantial funds on programs intended to aid homeless people, and such programs attract widespread public support. In recent years, however, state and local governments have increasingly enacted policies, such as bans on panhandling and sleeping in public, that are counterproductive to alleviating homelessness. Yet these policies also garner substantial support from the public. Given that programs aiding the homeless are so popular, why are these counterproductive policies also popular? We argue that disgust plays a key role in the resolution of this puzzle. While disgust does not decrease support for aid policies or even generate negative affect towards homeless people, it motivates the desire for physical distance, leading to support for policies that exclude homeless people from public life. We test this argument using survey data, including a national sample with an embedded experiment. Consistent with these expectations, our findings indicate that those respondents who are dispositionally sensitive to disgust are more likely to support exclusionary policies, such as banning panhandling, but no less likely to support policies intended to aid homeless people. Furthermore, media depictions of the homeless that include disease cues activate disgust, increasing its impact on support for banning panhandling. These results help explain the popularity of exclusionary homelessness policies and challenge common perspectives on the role of group attitudes in public life.Accepted manuscrip

Case Study: Election Observation Dispatches From the Polls

Provides an overview of the diversification among poll observers, from political parties to researchers to journalists and bloggers, and what they may contribute to the voting process. Summarizes state rules on media and public access to polling places

Numerical methods for coupled reconstruction and registration in digital breast tomosynthesis.

Digital Breast Tomosynthesis (DBT) provides an insight into the fine details of normal fibroglandular tissues and abnormal lesions by reconstructing a pseudo-3D image of the breast. In this respect, DBT overcomes a major limitation of conventional X-ray mam- mography by reducing the confounding effects caused by the superposition of breast tissue. In a breast cancer screening or diagnostic context, a radiologist is interested in detecting change, which might be indicative of malignant disease. To help automate this task image registration is required to establish spatial correspondence between time points. Typically, images, such as MRI or CT, are first reconstructed and then registered. This approach can be effective if reconstructing using a complete set of data. However, for ill-posed, limited-angle problems such as DBT, estimating the deformation is com- plicated by the significant artefacts associated with the reconstruction, leading to severe inaccuracies in the registration. This paper presents a mathematical framework, which couples the two tasks and jointly estimates both image intensities and the parameters of a transformation. Under this framework, we compare an iterative method and a simultaneous method, both of which tackle the problem of comparing DBT data by combining reconstruction of a pair of temporal volumes with their registration. We evaluate our methods using various computational digital phantoms, uncom- pressed breast MR images, and in-vivo DBT simulations. Firstly, we compare both iter- ative and simultaneous methods to the conventional, sequential method using an affine transformation model. We show that jointly estimating image intensities and parametric transformations gives superior results with respect to reconstruction fidelity and regis- tration accuracy. Also, we incorporate a non-rigid B-spline transformation model into our simultaneous method. The results demonstrate a visually plausible recovery of the deformation with preservation of the reconstruction fidelity

Expectation Propagation for Poisson Data

The Poisson distribution arises naturally when dealing with data involving counts, and it has found many applications in inverse problems and imaging. In this work, we develop an approximate Bayesian inference technique based on expectation propagation for approximating the posterior distribution formed from the Poisson likelihood function and a Laplace type prior distribution, e.g., the anisotropic total variation prior. The approach iteratively yields a Gaussian approximation, and at each iteration, it updates the Gaussian approximation to one factor of the posterior distribution by moment matching. We derive explicit update formulas in terms of one-dimensional integrals, and also discuss stable and efficient quadrature rules for evaluating these integrals. The method is showcased on two-dimensional PET images.Comment: 25 pages, to be published at Inverse Problem

WETAIR: A computer code for calculating thermodynamic and transport properties of air-water mixtures

A computer program subroutine, WETAIR, was developed to calculate the thermodynamic and transport properties of air water mixtures. It determines the thermodynamic state from assigned values of temperature and density, pressure and density, temperature and pressure, pressure and entropy, or pressure and enthalpy. The WETAIR calculates the properties of dry air and water (steam) by interpolating to obtain values from property tables. Then it uses simple mixing laws to calculate the properties of air water mixtures. Properties of mixtures with water contents below 40 percent (by mass) can be calculated at temperatures from 273.2 to 1497 K and pressures to 450 MN/sq m. Dry air properties can be calculated at temperatures as low as 150 K. Water properties can be calculated at temperatures to 1747 K and pressures to 100 MN/sq m. The WETAIR is available in both SFTRAN and FORTRAN

Clean Evidence on Face-to-Face: Why Experimental Economics is of Interest to Regional Economists

The notion of face-to-face contacts has recently become very popular in regional economics and in economic geography. This is the most obvious way to explain why firms still locate in proximity to others after the "death of distance", i.e., the shrinking costs for transportation, especially transportation of messages' pure information content. While this is intuitive, controlled laboratory experiments provide much more direct and reliable evidence on the importance of face-to-face contacts. They tackle the question what personal contacts are good for, and in which cases their effects are negligible. To the best of my knowledge, regional economists and geographers are not aware of this new and developing string of literature; it is the purpose of this paper to survey and to organize the relevant experimental research with a special focus on its importance for regional economics. However, the paper might also serve to alert more experimentalists to the importance of their work for current regional science, of which they seem not to be aware either.Cooperation, death of distance, face-to-face, localized spillovers, trust