Social cognitions about adult male victims of female sexual assault

Although men are far less likely than women to be victims of heterosexual assault, such cases have been reported with increasing frequency in recent years. We compared social judgments about male and female victims of heterosexual and homosexual rape and tested hypotheses concerning social cognitions that are assumed to underlie a male rape mythology. In a 2 × 2 × 2 design, 77 male and 89 female subjects made a series of judgments about a sexual assault case in which sex of victim and sex of assailants were manipulated. Consistent with the hypotheses, the male victim of sexual assault by females was judged more likely to have initiated or encouraged the sex acts, and more enjoyment and less stress were attributed to him. This pattern of results was more pronounced among male subjects. The results are discussed in relation to stereotypic beliefs concerning male sex roles, sexual motivation, and sexual functioning that are likely to affect the social cognitions of both observers and male victims of heterosexual assault.     

Bayesian analysis of two-component mixture distributions applied to estimating malaria attributable fractions

Malaria illness can be diagnosed by the presence of fever and parasitaemia. However, in highly endemic areas the diagnosis of clinical malaria can be difficult since children may tolerate parasites without fever and may have fever due to other causes. We propose a novel, simulation-based Bayesian approach for obtaining precise estimates of the probabilities of children with different levels of parasitaemia having fever due to malaria, by formulating the problem as a mixture of distributions. The methodology suggested is a general methodology for decomposing any two-component mixture distribution nonparametrically, when an independent training sample is available from one of the components. It is based on the assumption that one of the component distributions lies on the left of the other but there is some overlap between the distributions.     

Giving credit where credit is due: Dorothy Swaine Thomas and the “Thomas Theorem”

This study examines how Dorothy Swaine Thomas’s connection to the well-known “Thomas Theorem” is documented in introductory sociology texts. W.I. Thomas and Dorothy Swaine Thomas co-authoredThe Child in America (1928) in which the “theorem” first appears. However, it was not until the mid-1970s that Dorothy Swaine Thomas’s connection to these words begins to be cited in the books surveyed. The author suggests one reason for this pattern of neglect is a professional ideology that encouraged a process of genderization in sociology. It is only when women start to gain more visibility in the discipline that Dorothy Swain Thomas begins to be cited. The various ways the texts differ from the basic norms of citation are analyzed and discussed.

相似文献   

Properties of the bridge sampler with a focus on splitting the MCMC sample

Computation of normalizing constants is a fundamental mathematical problem in various disciplines, particularly in Bayesian model selection problems. A sampling-based technique known as bridge sampling (Meng and Wong in Stat Sin 6(4):831–860, 1996) has been found to produce accurate estimates of normalizing constants and is shown to possess good asymptotic properties. For small to moderate sample sizes (as in situations with limited computational resources), we demonstrate that the (optimal) bridge sampler produces biased estimates. Specifically, when one density (we denote as $$p_2$$) is constructed to be close to the target density (we denote as $$p_1$$) using method of moments, our simulation-based results indicate that the correlation-induced bias through the moment-matching procedure is non-negligible. More crucially, the bias amplifies as the dimensionality of the problem increases. Thus, a series of theoretical as well as empirical investigations is carried out to identify the nature and origin of the bias. We then examine the effect of sample size allocation on the accuracy of bridge sampling estimates and discovered that one possibility of reducing both the bias and standard error with a small increase in computational effort is by drawing extra samples from the moment-matched density $$p_2$$ (which we assume easy to sample from), provided that the evaluation of $$p_1$$ is not too expensive. We proceed to show how the simple adaptive approach we termed “splitting” manages to alleviate the correlation-induced bias at the expense of a higher standard error, irrespective of the dimensionality involved. We also slightly modified the strategy suggested by Wang et al. (Warp bridge sampling: the next generation, Preprint, 2019. arXiv:1609.07690) to address the issue of the increase in standard error due to splitting, which is later generalized to further improve the efficiency. We conclude the paper by offering our insights of the application of a combination of these adaptive methods to improve the accuracy of bridge sampling estimates in Bayesian applications (where posterior samples are typically expensive to generate) based on the preceding investigations, with an application to a practical example.     

Volunteering Hierarchies in the Global South: Remuneration and Livelihoods

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - This paper explores volunteering and inequality in the global South through an analysis of volunteering remuneration. We...