Disentangling the Bidirectional Relationships Across the Corporate Sustainable Development Indicators

Social Indicators Research - This study disentangles the relationships that exist between the four indicators of corporate sustainable development: economic, environmental, social, and governance....     

The praxis of ethnic studies: transforming second sight into critical consciousness

This article focuses on a youth participatory action research (YPAR) program called the Social Justice Education Project (SJEP) that fostered young people of color’s critical consciousness. Their critical consciousness emerged through praxis (reflection/action) while focusing on preserving ethnic studies in Tucson, Arizona. Because the SJEP home was in ethnic studies, the youth also struggled to keep their program alive. The Arizona Department of Education claimed the program bred ‘radicals’ who wanted to overthrow the government and therefore lobbied the state legislature to ban K-12 ethnic studies in public schools. In January 2012, the ban went into effect, shutting down ethnic studies classes as well as the SJEP. Young people’s qualitative research on their struggle led to action to save the education that gave them hope for a more equitable and just world. The article addresses the praxis of YPAR, which sparks a thought process leading to the drive to take action. Observing and documenting educational injustices inspire the need to seek radical change of Self and schools. Through the reflection and action facilitated by YPAR, young people of color construct a message about the importance of ethnic studies for individual as well as social transformation.     

Paired comparisons analysis: an axiomatic approach to ranking methods

In this paper we present an axiomatic analysis of several ranking methods for general tournaments. We find that the ranking method obtained by applying maximum likelihood to the (Zermelo-)Bradley-Terry model, the most common method in statistics and psychology, is one of the ranking methods that perform best with respect to the set of properties under consideration. A less known ranking method, generalised row sum, performs well too. We also study, among others, the fair bets ranking method, widely studied in social choice, and the least squares method.     

Homicide Rates in a Cross‐Section of Countries: Evidence and Interpretations

This study uses a regression analysis to explore the cross‐country variation in homicide rates for a large sample of countries. It starts by identifying seven significant regional variables, to which traditional socioeconomic, cultural, and institutional variables are added and tested. The importance of institutions, culture, and other factors affecting homicide rates is discussed. One unexpected finding is a curious relationship between the level of education and homicide rates: while an increase in male education tends to reduce homicide rates, an increase in female education tends to increase homicides. Several possible interpretations for this phenomenon are proposed. The study points to relatively unexplored areas of research in order to better understand homicide variation around the world.     

Breaking in a new function for casein kinase 2

DNA damage response mechanisms help ensure the fidelity of chromosomal transmission, and the failure of such mechanisms might lead to premature aging and cancer. A new report has established that casein kinase 2 (CK2), a protein that functions in diverse cellular processes, controls the activity of the DNA repair protein XRCC1. These results indicate that CK2 is a key participant in the cellular response to DNA damage.     

Hierarchical Variable Selection in Polynomial Regression Models

Significance tests on coefficients of lower-order terms in polynomial regression models are affected by linear transformations. For this reason, a polynomial regression model that excludes hierarchically inferior predictors (i.e., lower-order terms) is considered to be not well formulated. Existing variable-selection algorithms do not take into account the hierarchy of predictors and often select as “best” a model that is not hierarchically well formulated. This article proposes a theory of the hierarchical ordering of the predictors of an arbitrary polynomial regression model in m variables, where m is any arbitrary positive integer. Ways of modifying existing algorithms to restrict their search to well-formulated models are suggested. An algorithm that generates all possible well-formulated models is presented.     

Design-based random permutation models with auxiliary information

We extend the random permutation model to obtain the best linear unbiased estimator of a finite population mean accounting for auxiliary variables under simple random sampling without replacement (SRS) or stratified SRS. The proposed method provides a systematic design-based justification for well-known results involving common estimators derived under minimal assumptions that do not require specification of a functional relationship between the response and the auxiliary variables.     

Constrained Efficiency in the Neoclassical Growth Model With Uninsurable Idiosyncratic Shocks

We investigate the welfare properties of the one‐sector neoclassical growth model with uninsurable idiosyncratic shocks. We focus on the notion of constrained efficiency used in the general equilibrium literature. Our characterization of constrained efficiency uses the first‐order condition of a constrained planner's problem. This condition highlights the margins of relevance for whether capital is too high or too low: the factor composition of income of the (consumption‐)poor. Using three calibrations commonly considered in the literature, we illustrate that there can be either over‐ or underaccumulation of capital in steady state and that the constrained optimum may or may not be consistent with a nondegenerate long‐run distribution of wealth. For the calibration that roughly matches the income and wealth distribution, the constrained inefficiency of the market outcome is rather striking: it has much too low a steady‐state capital stock.     

A new regression model for bimodal data and applications in agriculture

We define the odd log-logistic exponential Gaussian regression with two systematic components, which extends the heteroscedastic Gaussian regression and it is suitable for bimodal data quite common in the agriculture area. We estimate the parameters by the method of maximum likelihood. Some simulations indicate that the maximum-likelihood estimators are accurate. The model assumptions are checked through case deletion and quantile residuals. The usefulness of the new regression model is illustrated by means of three real data sets in different areas of agriculture, where the data present bimodality.