Finding Bhaskar in all the wrong places? Causation,process, and structure in Bhaskar and Deleuze

This article examines the reception of Roy Bhaskar amongst some contemporary Deleuzians. It proceeds by rejecting the all too often predilection of opposing realism to ‘postmodernism’ or ‘post‐structuralism’ arguing instead for the need to bring one into dialogue with the other. To this end, the paper explores the resonances and points of departure between the work of Gilles Deleuze and Roy Bhaskar. In particular, it examines the language of causation, object‐oriented versus process‐oriented ontologies, as well as the charge by Deleuzians that Bhaskar is an essentialist. Through this engagement it attempts to develop and rethink explanation and causation in terms of a more chaotic ontology of machines, centered around the concept of structure, process, and production in an open, heterogeneous, and dynamic world. The end result is a more chaotic concept of realism.     

Dublin’s Seventeenth Century Parishes and the Quality of Life

Social Indicators Research - The registers of Dublin’s parishes in the seventeenth century provide access to aspects of civic and religious life. In the registers are records of burials,...     

Estimating urban lawn cover in space and time: Case studies in three Swedish cities

Lawns are considered monocultures and lesser contributors to sustainability than diverse nature but are still a dominating green area feature and an important cultural phenomenon in cities. Lawns have esthetical values, provide playground, are potential habitat for species, contribute to carbon sequestration and water infiltration, but also increase pesticides, fertilization, are monocultures and costly to manage at the same time. To evaluate the potential impact of lawns, whether positive or negative, it is of interest to estimate the total lawn cover in cities and its change over time. This is not a straightforward process, e.g., because many lawns are small and covered by trees. In this study we review the existing literature of lawn cover in cities and the different methodologies used for cover estimation. We found both pros and cons with NDVI and LiDAR data as well as manually interpreted aerial photos. The total cover of lawns in three case study cities was estimated to 22.5%. By extrapolating these percentages to all Swedish cities lawn cover was estimated to 2589 km² (0.6% of the terrestrial surface). The approximated total municipal management cost of lawns in all Swedish cities was 910,000,000 USD/ year. During 50 years lawn area almost doubled in relative cover and 56% of them were continuously managed. Since lawns constitute large parts of the urban greenery and are costly to manage it is highly relevant to consider their social, ecological and cultural value compared to alternatives, e.g., meadows with less intensive management.     

The Risk of James–Stein and Lasso Shrinkage

This article compares the mean-squared error (or ?₂ risk) of ordinary least squares (OLS), James–Stein, and least absolute shrinkage and selection operator (Lasso) shrinkage estimators in simple linear regression where the number of regressors is smaller than the sample size. We compare and contrast the known risk bounds for these estimators, which shows that neither James–Stein nor Lasso uniformly dominates the other. We investigate the finite sample risk using a simple simulation experiment. We find that the risk of Lasso estimation is particularly sensitive to coefficient parameterization, and for a significant portion of the parameter space Lasso has higher mean-squared error than OLS. This investigation suggests that there are potential pitfalls arising with Lasso estimation, and simulation studies need to be more attentive to careful exploration of the parameter space.     

Assessing Value Differences Between Leaders of Two Social Venture Types: Benefit Corporations and Nonprofit Organizations

This study considers key value differences between leaders of two types of social ventures: for-profit social benefit corporations and nonprofit organizations. The research question asks to what degree the value sets of leaders of benefit corporations are congruent with those of similarly situated individuals in nonprofit organizations. The results show the values of leaders working in benefit corporations and nonprofit organizations are in many ways aligned, but there are notable statistical differences. A sectoral association of values is also present with employees in both types of organization, especially when the previous work experience of employees is considered.     

How do Australian adult mental health clinicians manage the challenges of working with parental mental illness? A phenomenological study

Adult mental health clinicians face a range of challenges that hinder their use of family‐focused practices when working with consumers who are parents. The purpose of this study was (a) to examine clinicians' experiences when working with parents and (b) identify strategies they found to be effective when working with parents. Eleven Australian mental health clinicians were recruited who regularly worked with consumers who are parents. Semistructured interviews were conducted within a qualitative paradigm and analysed using interpretative phenomenological analysis. Themes were identified which related to (a) managing sensitive parenting conversations, (b) making decisions about child safety in unclear or unpredictable situations, and (c) working with child protection services. Participants had developed strategies for managing the challenges of the first two practice issues and suggested organizational changes that could facilitate collaboration with child protection services. The findings highlight that the challenges of working with parents with mental health issues cannot be addressed with a one‐size‐fits‐all approach. Initiatives to facilitate the effective support of parents and their children need to be informed by contextual factors, including clinical practice.     

Asymptotic theory for maximum likelihood estimates in reduced-rank multivariate generalized linear models

Reduced-rank regression is a dimensionality reduction method with many applications. The asymptotic theory for reduced rank estimators of parameter matrices in multivariate linear models has been studied extensively. In contrast, few theoretical results are available for reduced-rank multivariate generalized linear models. We develop M-estimation theory for concave criterion functions that are maximized over parameter spaces that are neither convex nor closed. These results are used to derive the consistency and asymptotic distribution of maximum likelihood estimators in reduced-rank multivariate generalized linear models, when the response and predictor vectors have a joint distribution. We illustrate our results in a real data classification problem with binary covariates.     

Quantifying the Value of Collecting: Implications for Financial Advisers

This paper documents the extent to which collectors—specifically, those owning collectible classic US postage stamps—experience an opportunity cost associated with expenditures on their collection. Results show, based on stamp price, S&P 500, bond, and T-bill rate data over the period 1969 through 2013, that collectible stamps tend to underperform stocks and bonds on a risk-adjusted basis. Using estimates based on the Modigliani measure (M²), it was determined that collectors incur an opportunity cost when selecting collectible stamps over more traditional investments. However, it is known that collecting as a hobby provides sociological and psychological benefits. This paper adds to the literature by illustrating how collecting also provides psychic return benefits that can be valued similarly to investment returns. In this study, the foregone return rate of stamp collecting for those who allocate a significant percent of available resources to their collection equates to between 3 and 13 % on an annual basis.     

On Modeling Wood Formation Using Parametric and Semiparametric Regressions for Count Data

Understanding how wood develops has become an important problematic of plant sciences. However, studying wood formation requires the acquisition of count data difficult to interpret. Here, the annual wood formation dynamics of a conifer tree species were modeled using generalized linear and additive models (GLM and GAM); GAM for location, scale, and shape (GAMLSS); a discrete semiparametric kernel regression for count data. The performance of models is evaluated using bootstrap methods. GLM was useful to describe the wood formation general pattern but had a lack of fitting, while GAM, GAMLSS, and kernel regression had a higher sensibility to short-term variations.