Multicriteria Decision Analysis: A Comprehensive Decision Approach for Management of Contaminated Sediments

Contaminated sediments and other sites present a difficult challenge for environmental decisionmakers. They are typically slow to recover or attenuate naturally, may involve multiple regulatory agencies and stakeholder groups, and engender multiple toxicological and ecotoxicological risks. While environmental decision-making strategies over the last several decades have evolved into increasingly more sophisticated, information-intensive, and complex approaches, there remains considerable dissatisfaction among business, industry, and the public with existing management strategies. Consequently, contaminated sediments and materials are the subject of intense technology development, such as beneficial reuse or in situ treatment. However, current decision analysis approaches, such as comparative risk assessment, benefit-cost analysis, and life cycle assessment, do not offer a comprehensive approach for incorporating the varied types of information and multiple stakeholder and public views that must typically be brought to bear when new technologies are under consideration. Alternatively, multicriteria decision analysis (MCDA) offers a scientifically sound decision framework for management of contaminated materials or sites where stakeholder participation is of crucial concern and criteria such as economics, environmental impacts, safety, and risk cannot be easily condensed into simple monetary expressions. This article brings together a multidisciplinary review of existing decision-making approaches at regulatory agencies in the United States and Europe and synthesizes state-of-the-art research in MCDA methods applicable to the assessment of contaminated sediment management technologies. Additionally, it tests an MCDA approach for coupling expert judgment and stakeholder values in a hypothetical contaminated sediments management case study wherein MCDA is used as a tool for testing stakeholder responses to and improving expert assessment of innovative contaminated sediments technologies.     

A simulation based method for assessing the statistical significance of logistic regression models after common variable selection procedures

Classification models can demonstrate apparent prediction accuracy even when there is no underlying relationship between the predictors and the response. Variable selection procedures can lead to false positive variable selections and overestimation of true model performance. A simulation study was conducted using logistic regression with forward stepwise, best subsets, and LASSO variable selection methods with varying total sample sizes (20, 50, 100, 200) and numbers of random noise predictor variables (3, 5, 10, 15, 20, 50). Using our critical values can help reduce needless follow-up on variables having no true association with the outcome.     

Hybrid Masculinities: New Directions in the Sociology of Men and Masculinities

Hybrid masculinity refers to men's selective incorporation of performances and identity elements associated with marginalized and subordinated masculinities and femininities. We use recent theorization of hybrid masculinities to critically review theory and research that seeks to make sense of contemporary transformations in masculinity. We suggest that research broadly supports three distinct consequences associated with recent changes in performances and politics of masculinity that work to obscure the tenacity of gendered inequality. Hybrid masculinities (i) symbolically distance men from hegemonic masculinity; (ii) situate the masculinities available to young, White, heterosexual men as somehow less meaningful than the masculinities associated with various marginalized and subordinated Others; and (iii) fortify existing social and symbolic boundaries in ways that often work to conceal systems of power and inequality in historically new ways.     

Resilience for gender inclusion: Developing a model for women in male-dominated occupations

What is resilience in the context of work environments where women face barriers as significant as career discrimination and harassment? In such a context, is resilience an individual responsibility? How can organizations contribute to and support employee resilience? And where is gender in this equation? This conceptual paper explores these questions using aviation, the skilled trades (i.e., carpentry, welding, plumbing) and the military as case studies to understand how gender inclusion could be better supported by resilient organizations. The barriers for women in male-dominated industries include social exclusion, marginalization, discrimination, harassment, and other forms of social closure. How these barriers can be overcome is not well understood. We argue that individual resilience plays a part in women thriving and developing enduring careers but can only occur in combination with support from gender inclusion strategies and organizational resilience. We have developed the Resilience for Gender Inclusion (RGI) model combining gender inclusion strategies with organizational resilience strategies. The RGI model demonstrates how employee and organizational resilience intersect and may lead to the transformative potential of inclusive cultures of diversity. This will improve employee wellbeing and self-efficacy and create a much needed sense of belonging and social inclusion for women in male-dominated occupations.     

What Policies Advance Infants and Toddlers? Evidence to Inform State and Federal Options

A variety of civic actors—government, associations, and local agencies—work to help parents advance the vitality of our youngest children. Empirical findings accumulating over the past half-century identify benefits for infants and toddlers stemming from three policy models: paid leave for parents after a newborn arrives; regular pediatric assessments, including home visiting; and quality caregivers situated in homes or centers. We review what is known about the effects of these policies, along with constituent elements of quality (mediators) that operate proximal to children's health, cognitive, and emotional growth. Much has been learned about how such collective action, carried out by local organizations, advance infant–toddler development. Methodological advances foster new knowledge: moving closer to causal inferences and pinpointing social mechanisms that enrich infant–toddler settings. Less well understood is how policy levers can move the malleable elements of program quality to raise the magnitude or sustainability of program effects. We note the benefits of income-support efforts for fragile families, while urging new work on how economic dynamics touch the capacity of parents and caregivers to better nurture infants and toddlers.     

Discrete Nonparametric Kernel and Parametric Methods for the Modeling of Pavement Deterioration

This article is concerned with one discrete nonparametric kernel and two parametric regression approaches for providing the evolution law of pavement deterioration. The first parametric approach is a survival data analysis method; and the second is a nonlinear mixed-effects model. The nonparametric approach consists of a regression estimator using the discrete associated kernels. Some asymptotic properties of the discrete nonparametric kernel estimator are shown as, in particular, its almost sure consistency. Moreover, two data-driven bandwidth selection methods are also given, with a new theoretical explicit expression of optimal bandwidth provided for this nonparametric estimator. A comparative simulation study is realized with an application of bootstrap methods to a measure of statistical accuracy.     

Rethinking gender segregation and gender inequality: Measures and meanings

It is often assumed that occupational segregation by gender is readily interpretable as an index of inequality between men and women in the labor force. Although this view has been challenged, the development and dissemination of analytic tools that could test this assumption have been limited. This article reviews two methods for identifying invidious and noninvidious components of gender segregation and develops a third approach that overcomes some limitations of the other two. Each method is applied to a data set that consists of observations from 22 countries covered in the International Social Survey Program. The results are compared and contrasted, and several conclusions emerge. First, occupational segregation is not completely interpretable as occupational gender inequality. Second, the methods differ in how large they estimate the invidious (vertical) component to be. Third, each method produces measures of invidious and noninvidious segregation that can be predicted statistically from other characteristics of the countries in question. The article ends with suggestions about the circumstances under which the various approaches may be the most useful.     

A multivariate non-parametric kernel estimator for global sensitivity analysis

The fact of estimating how a model output is influenced by the variations of inputs has become an important problematic in reliability and sensitivity analysis. This article is interested in estimating sensitivity indices useful to quantify the contribution of inputs to the variance of model output. A multivariate mixed kernel estimator is investigated since, until now, discrete and continuous inputs have been separately considered in kernel estimation of sensitivity indices. To illustrate the differences between the influence of mixed, discrete, and continuous inputs, analytical expressions of Sobol sensitivity indices are expressed in these three cases for the Ishigami test function. Besides, the performance of the mixed kernel estimator is illustrated through simulations in which the Bayesian procedure is applied for bandwidth parameter choice. An application is also realized on a real example. Finally, the use of an appropriate kernel estimator according to the type of inputs is found to be influential on the accuracy of sensitivity indices estimates.