Finding a Disappearing Nontimber Forest Resource: Using Grounded Visualization to Explore Urbanization Impacts on Sweetgrass Basketmaking in Greater Mt. Pleasant, South Carolina

Despite growing interest in urbanization and its social and ecological impacts on formerly rural areas, empirical research remains limited. Extant studies largely focus either on issues of social exclusion and enclosure or ecological change. This article uses the case of sweetgrass basketmaking in Mt. Pleasant, South Carolina, to explore the implications of urbanization, including gentrification, for the distribution and accessibility of sweetgrass, an economically important nontimber forest product (NTFP) for historically African American communities, in this rapidly growing area. We explore the usefulness of grounded visualization for research efforts that are examining the existence of fringe ecologies associated with NTFP. Our findings highlight the importance of integrated qualitative and quantitative analyses for revealing the complex social and ecological changes that accompany both urbanization and rural gentrification

Multidimensional Signals and Analytic Flexibility: Estimating Degrees of Freedom in Human-Speech Analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis that can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling but also from decisions regarding the quantification of the measured behavior. In this study, we gave the same speech-production data set to 46 teams of researchers and asked them to answer the same research question, resulting in substantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further found little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise, or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system, and calibrate their (un)certainty in their conclusions

Multidimensional signals and analytic flexibility: Estimating degrees of freedom in human speech analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis which can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling, but also from decisions regarding the quantification of the measured behavior. In the present study, we gave the same speech production data set to 46 teams of researchers and asked them to answer the same research question, resulting insubstantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further find little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system and calibrate their (un)certainty in their conclusions

A push-pull framework for modelling the relocation of retirees to a retirement village: The Australian experience

Although most older people prefer to age in place, nonetheless many do relocate, with a small proportion moving to retirement villages, which provide a purpose designed and built residential and lifestyle environment. Using factor analyses, path analyses, and a push-pull framework, the authors model the decision process of retirees in Australia in order to identify relationships between push-pull factors and predictor variables, using data from a national survey of retirement village residents. The push factors relate to change in lifestyle, home maintenance, social isolation, and health and mobility, whereas the pull factors relate to built environment and affordability, the locational attributes of villages, and the desire to maintain an existing lifestyle. The survey data also identify village attributes considered desirable or undesirable, or important or unimportant. Overall, resident satisfaction with moving is high

Tropospheric Halogen Chemistry:Sources, Cycling, and Impacts

In the past 40 years, atmospheric chemists have come to realize that halogens exert a powerful influence on the chemical composition of the troposphere and through that influence affect the fate of pollutants and may affect climate. Of particular note for climate is that halogen cycles affect methane, ozone, and particles, all of which are powerful climate forcing agents through direct and indirect radiative effects. This influencecomes from the high reactivity of atomic halogen radicals (e.g.,Cl, Br, I) and halogen oxides (e.g., ClO, BrO, IO, and higher oxides), known as reactive halogen species in this review. These reactive halogens are potent oxidizers for organic and inorganic compounds throughout the troposphere