“Midwife to Myself”: Birth Narratives among Women Choosing Unassisted Homebirth*

Most pregnant women in the United States today choose a normative physician‐guided pregnancy followed by a medically managed hospital birth. Some, however, choose the care of a midwife during pregnancy and birth, whether in the hospital or, more rarely, at home. Despite growing research on both these paths, a third option chosen by some women has rarely been studied: a planned birth at home with neither a doctor nor a midwife assisting. In this article, I examine the stories told by women in this under‐researched population to consider how they make and explain this highly unusual choice. Analysis of online birth stories and in‐depth interviews with women who planned and had an unassisted homebirth reveal ways in which these women rely on competing discourses of midwifery and medicine to craft a unique sense of agency in birth.     

Methods for distance-based judgment aggregation

Judgment aggregation theory, which concerns the translation of individual judgments on logical propositions into consistent group judgments, has shown that group consistency generally cannot be guaranteed if each proposition is treated independently from the others. Developing the right method of abandoning independence is thus a high-priority goal. However, little work has been done in this area outside of a few simple approaches. To fill the gap, we compare four methods based on distance metrics between judgment sets. The methods generalize the premise-based and sequential priority approaches to judgment aggregation, as well as distance-based preference aggregation. They each guarantee group consistency and implement a range of distinct functions with different properties, broadening the available tools for social choice. A central result is that only one of these methods (not previously considered in the literature) satisfies three attractive properties for all reasonable metrics.     

General lack of fit tests based on families of groupings

Lack of fit tests based on groupings of the observations are developed. These tests are first applied to models with replication. In this case, the classic Fisher test assumes that the true model is contained in the one-way ANOVA model. However, Christensen [(2003). Significantly insignificant F tests. Amer. Statist. 57, 27–32] has noted that small values of the F-statistic may indicate lack of fit due to features which are not part of the proposed model. Such model inadequacy is called within-cluster lack of fit, whereas the standard Fisher lack of fit is called between-cluster lack of fit. Typically, lack of fit exists as a combination of these two pure types, and can be extremely difficult to detect depending on the nature of the mixture. In this paper, the one-way ANOVA model is embedded in larger models using groupings of the observations, which provides tests with good power for detecting all of the above types of model inadequacies, including mixtures. In particular, several such tests are considered, each based on a different grouping of the observations, and the multiple testing approach of Baraud et al. [(2003). Adaptive tests of linear hypotheses by model selection. Ann. Statist. 31, 225–251] is followed. More generally, the preceding testing procedure based on families of groupings is extended to the case of nonreplication. For this case, it is proposed that such families be determined by linear orders on the predictors based on disjoint parallel tubes in predictor space. Test statistics follow the cluster-based regression lack of fit tests presented by Christensen [(1989). Lack of fit based on near or exact replicates. Ann. Statist. 17, 673–683; (1991). Small sample characterizations of near replicate lack of fit tests. J. Amer. Statist. Assoc. 86, 752–756], by considering the groupings as determining special types of clusterings. In order to detect general lack of fit, several such tests are again considered, each based on a different grouping of the observations, and the multiple testing approach given by Baraud et al. [(2003). Adaptive tests of linear hypotheses by model selection. Ann. Statist. 31, 225–251] is followed. Simulation results illustrating the power of the proposed testing procedure are given.     

Gambling Revenues as a Public Administration Issue: Electronic Gaming Machines in Victoria

Gambling activities and the revenues derived have been seen as a way to increase economic development in deprived areas. There are also, however, concerns about the effects of gambling in general and electronic gaming machines (EGMs) in particular, on the resources available to the localities in which they are situated. This paper focuses on the factors that determine the extent and spending of community benefit-related EGM-generated resources within Victoria, Australia, focusing in particular on the relationships between EGM activity and socio-economic and social capital indicators, and how this relates to the community benefit resources generated by gaming.