Mad, Bad, or Reasonable? Newspaper Portrayals of the Battered Woman Who Kills

A heated debate about battered women who kill abusive male partners started in the 1970s. In this study, we tracked the public discourse on battered women who kill by coding 250 newspaper articles published between 1978 and 2002. Using four typifying models, we found that leading explanations for why battered women kill medicalized then criminalized their actions; they were mad then bad. We also found that reporters used quotes from claims makers supporting conventional or medical typifications of battered women to a much greater degree than statements from alternative, feminist sources. In conclusion, simplified, sensational and conventional understandings of crime causation drove the social construction of “the battered woman who kills”. She may be mad or bad, but rarely has she been portrayed as reasonable. Suggestions for promoting feminist narrative in the media are also provided.     

Sample Selection in Models of Academic Performance

This article shows how admission and enrollment processes affect the interpretation of simple validation studies of academic performance. In a competitive market for students, optimal behavior of admissions committees and applicants drives the simple correlation between test scores and performance toward zero, regardless of the relationship in the population of prospective students. Data from our university's MBA program support the prediction that applicants exhibit a higher correlation between test scores and undergraduate GPAs than do current students. This suggests that standard validation studies will understate the importance of GMAT scores in predicting performance of potential MBA students. (JEL C24 , I121 , C52 )     

Bayesian inference for pairwise interacting point processes

Pairwise interacting point processes are commonly used to model spatial point patterns. To perform inference, the established frequentist methods can produce good point estimates when the interaction in the data is moderate, but some methods may produce severely biased estimates when the interaction in strong. Furthermore, because the sampling distributions of the estimates are unclear, interval estimates are typically obtained by parametric bootstrap methods. In the current setting however, the behavior of such estimates is not well understood. In this article we propose Bayesian methods for obtaining inferences in pairwise interacting point processes. The requisite application of Markov chain Monte Carlo (MCMC) techniques is complicated by an intractable function of the parameters in the likelihood. The acceptance probability in a Metropolis-Hastings algorithm involves the ratio of two likelihoods evaluated at differing parameter values. The intractable functions do not cancel, and hence an intractable ratio r must be estimated within each iteration of a Metropolis-Hastings sampler. We propose the use of importance sampling techniques within MCMC to address this problem. While r may be estimated by other methods, these, in general, are not readily applied in a Bayesian setting. We demonstrate the validity of our importance sampling approach with a small simulation study. Finally, we analyze the Swedish pine sapling dataset (Strand 1972) and contrast the results with those in the literature.     

The spatial structure of epidemic emergence: geographical aspects of poliomyelitis in north-eastern USA, July-October 1916

Summary.  The great epidemic of poliomyelitis which swept New York City and surrounding territory in the summer of 1916 eclipsed all previous global experience of the disease. We draw on epidemiological information that is included in the seminal US Public Health Bulletin 91, 'Epidemiologic studies of poliomyelitis in New York City and the northeastern United States during the year 1916' (Washington DC, 1918), to re-examine the spatial structure of the epidemic. For the main phase of transmission of the epidemic, July–October 1916, it is shown that the maximum concentration of activity of poliomyelitis occurred within a 128-km radius of New York City. Although the integrity of the poliomyelitis cluster was maintained up to approximately 500 km from the metropolitan focus, the level and rate of propagation of disease declined with distance from the origin of the epidemic. Finally, it is shown that the geographical transmission of the epidemic in north-eastern USA probably followed a process of mixed contagious–hierarchical diffusion.     

Business drivers not sector membership determine the most effective production planning and control; a novel approach to a perennial problem

This paper outlines research aimed at developing novel planning and scheduling reference models for industrial sectors where the MRPII paradigm is not appropriate. It outlines the process mapping approach adopted, the data capture method developed for the case study companies, the use of ARIS (Scheer's) enterprise modelling tool, and the first stages in the production of sector reference models.     

Examining the Impact of Demographic Factors on Air Pollution

This study adds to the emerging literature examining empirically the link between population size, other demographic factors and pollution. We contribute by using more reliable estimation techniques and examine two air pollutants. By considering sulfur dioxide, we become the first study to explicitly examine the impact of demographic factors on a pollutant other than carbon dioxide at the cross-national level. We also take into account the urbanization rate and the average household size neglected by many prior cross-national econometric studies. For carbon dioxide emissions we find evidence that population increases are matched by proportional increases in emissions while a higher urbanization rate and lower average household size increase emissions. For sulfur dioxide emissions, we find a U-shaped relationship, with the population-emissions elasticity rising at higher population levels. Urbanization and average household size are not found to be significant determinants of sulfur dioxide emissions. For both pollutants, our results suggest that an increasing share of global emissions will be accounted for by developing countries. Implications for the environmental Kuznets curve literature are described and directions for further work identified.     

Social Isolation and Lethal Violence Across the Metro/Nonmetro Divide: The Effects of Socioeconomic Disadvantage and Poverty Concentration on Homicide*

Abstract This study extends the macro‐level criminological research tradition by examining the links between socioeconomic disadvantage, poverty concentration, and homicide in metropolitan and nonmetropolitan U.S. counties. Most research in this tradition has tested structural theories using urban areas as the unit of analysis. This “urban bias” has resulted in a limited understanding of the social forces driving violence in nonmetropolitan areas. To partially address this problem, we link the literature on the spatial and social organization of nonmetropolitan communities with the social isolation perspective from the urban poverty literature. We hypothesize that the spatial concentration of poverty drives up rates of homicide in both metropolitan and nonmetropolitan areas regardless of levels of socioeconomic disadvantage. Negative binomial regression for 1,746 nonmetropolitan and 778 metropolitan counties suggest that both socioeconomic disadvantage and poverty concentration elevate homicide in metropolitan areas. However, in nonmetropolitan counties only socioeconomic disadvantage has a significant impact. We conclude by discussing the implications of these differential findings for the social isolation perspective.