The Future of Environmental Justice Research: A Response to Gould and Weinberg

Sociological Forum -     

Cockpits,Hangars, Boys and Galleys: Corporate Masculinities and the Development of British Airways

This article sets out to examine the role of masculinity in the development of a gendered organizational culture over time. The development of images of masculinity within one company — British Airways — is examined through content analysis of company newsletters, advertising copy, annual reports, internal memoranda, and written rules and regulations. Exploring the notion of ‘multiple masculinities’, the article traces the prominent forms of masculinity that emerged in British Airways and assesses their impact on the ways that organizational practices were developed, maintained and understood. Four key corporate images of masculinity are examined — the pilot, the steward, the engineer and the ‘native boy’— and it is argued that those images contributed to the exclusion of women and people of colour from those occupations by laying down cultural rules about the ideal typical characteristics of the job holder. The article concludes by raising questions about the value of a multiple masculinities focus in explaining changing and contradictory practices of discrimination; the primacy of extra-organizational over organizational practices; and the relationship between multiple masculinities and hegemonic masculinity. Further research is suggested into the extent to which hegemonic masculinity is undermined, over time, by changing and contradictory forms of masculinity within definite sites of gender construction.     

Genotypic performance over environmental arrays: A non-linear grouping protocol

A non-linear model for examining genotypic responses across an array of environments is contrasted with the 'joint regression' formulation, and a rigorous approach to hypothesis testing using the conditional error principle is demonstrated. The model is extended to cater for situations where single straight-line response patterns fail to characterize genotypic behaviors over an environmental array: a combination of two straight lines, with slope in below-average and in above-average environments, is offered as the 1 2 simplest representation of convex and concave patterns. A protocol for classifying genotypes according to the results of hypothesis tests, i.e. H( = ) and H( = = = 1), is 1 2 1 2 presented . A doubly desirable response pattern is convex ( < 1< ), while a doubly 1 2 undesirable pattern is concave ( > 1> ). 1 2     

Sample selection in radiocarbon dating

Archaeologists working on the island of O'ahu, Hawai'i, use radiocarbon dating of samples of organic matter found trapped in fish-pond sediments to help them to learn about the chronology of the construction and use of the aquicultural systems created by the Polynesians. At one particular site, Loko Kuwili, 25 organic samples were obtained and funds were available to date an initial nine. However, on calibration to the calendar scale, the radiocarbon determinations provided date estimates that had very large variances. As a result, major issues of chronology remained unresolved and the archaeologists were faced with the prospect of another expensive programme of radiocarbon dating. This paper presents results of research that tackles the problems associated with selecting samples from those which are still available. Building on considerable recent research that utilizes Markov chain Monte Carlo methods to aid archaeologists in their radiocarbon calibration and interpretation, we adopt the standard Bayesian framework of risk functions, which allows us to assess the optimal samples to be sent for dating. Although rather computer intensive, our algorithms are simple to implement within the Bayesian radiocarbon framework that is already in place and produce results that are capable of direct interpretation by the archaeologists. By dating just three more samples from Loko Kuwili the expected variance on the date of greatest interest could be substantially reduced.     

Spatial variation in risk of disease: a nonparametric binary regression approach

A common problem in environmental epidemiology is the estimation and mapping of spatial variation in disease risk. In this paper we analyse data from the Walsall District Health Authority, UK, concerning the spatial distributions of cancer cases compared with controls sampled from the population register. We formulate the risk estimation problem as a nonparametric binary regression problem and consider two different methods of estimation. The first uses a standard kernel method with a cross-validation criterion for choosing the associated bandwidth parameter. The second uses the framework of the generalized additive model (GAM) which has the advantage that it can allow for additional explanatory variables, but is computationally more demanding. For the Walsall data, we obtain similar results using either the kernel method with controls stratified by age and sex to match the age–sex distribution of the cases or the GAM method with random controls but incorporating age and sex as additional explanatory variables. For cancers of the lung or stomach, the analysis shows highly statistically significant spatial variation in risk. For the less common cancers of the pancreas, the spatial variation in risk is not statistically significant.     

Application of Markov chain Monte Carlo methods to modelling birth prevalence of Down syndrome

Data collected before the routine application of prenatal screening are of unique value in estimating the natural live-birth prevalence of Down syndrome. However, much of these data are from births from over 20 years ago and they are of uncertain quality. In particular, they are subject to varying degrees of underascertainment. Published approaches have used ad hoc corrections to deal with this problem or have been restricted to data sets in which ascertainment is assumed to be complete. In this paper we adopt a Bayesian approach to modelling ascertainment and live-birth prevalence. We consider three prior specifications concerning ascertainment and compare predicted maternal-age-specific prevalence under these three different prior specifications. The computations are carried out by using Markov chain Monte Carlo methods in which model parameters and missing data are sampled.     

Finite mixture models with concomitant information: assessing diagnostic criteria for diabetes

The World Health Organization (WHO) diagnostic criteria for diabetes mellitus were determined in part by evidence that in some populations the plasma glucose level 2 h after an oral glucose load is a mixture of two distinct distributions. We present a finite mixture model that allows the two component densities to be generalized linear models and the mixture probability to be a logistic regression model. The model allows us to estimate the prevalence of diabetes and sensitivity and specificity of the diagnostic criteria as a function of covariates and to estimate them in the absence of an external standard. Sensitivity is the probability that a test indicates disease conditionally on disease being present. Specificity is the probability that a test indicates no disease conditionally on no disease being present. We obtained maximum likelihood estimates via the EM algorithm and derived the standard errors from the information matrix and by the bootstrap. In the application to data from the diabetes in Egypt project a two-component mixture model fits well and the two components are interpreted as normal and diabetes. The means and variances are similar to results found in other populations. The minimum misclassification cutpoints decrease with age, are lower in urban areas and are higher in rural areas than the 200 mg dl^-1 cutpoint recommended by the WHO. These differences are modest and our results generally support the WHO criterion. Our methods allow the direct inclusion of concomitant data whereas past analyses were based on partitioning the data.