The devil in high heels: drugs,symbolism and Kate Moss

This paper contributes to critical voices on the issue of organisational responses to employee drug use. It does so by exploring symbolic readings of organisations’ relations with drugs and drug‐taking. Our focus is recent coverage of, and organisational responses to, the UK tabloid media’s exposé of fashion supermodel Kate Moss’s alleged cocaine use. We consider that the celebrity endorsement in this particular case highlights the ambiguities created by the symbolic associations between the organisation and the ‘image’ projected by the celebrity. Overall, we use this case to explore symbolic relationships between drugs, sex, femininity and organisation. Through highlighting these connections, we question further the rationality of organisational responses to employee drug use and, utilising Derrida’s (1981 Derrida, J. 1981. Dissemination, London: Atholone Press.  [Google Scholar]) extension of Plato’s notion of the pharmakon, consider whether workforce drug testing might be fruitfully seen as a symbolic mechanism for scapegoating and sacrifice in order to protect the organisation’s (masculine) moral order.     

Learning classification trees

Algorithms for learning classification trees have had successes in artificial intelligence and statistics over many years. This paper outlines how a tree learning algorithm can be derived using Bayesian statistics. This introduces Bayesian techniques for splitting, smoothing, and tree averaging. The splitting rule is similar to Quinlan's information gain, while smoothing and averaging replace pruning. Comparative experiments with reimplementations of a minimum encoding approach,c4 (Quinlanet al., 1987) andcart (Breimanet al., 1984), show that the full Bayesian algorithm can produce more accurate predictions than versions of these other approaches, though pays a computational price.     

Relationship of service satisfaction to life satisfaction and perceived well-being

Two procedures for gaining patients' evaluations of health services were compared: (a) the Client Satisfaction Questionnaire (CSQ-18B), a direct approach assessing the setting and services actually encountered, and (b) the Patient Satisfaction Questionnaire (PSQ), an approach that indirectly assesses satisfaction with service by inquiring about general health care attitudes. Results from 148 public health patients indicated that the PSQ produced the most acceptability problems and was tapping aspects of life satisfaction other than service satisfaction. However, the PSQ produced significantly lower reports of satisfaction. Additional comparison and interpretation of typical responses generated from the two approaches suggested, on the whole, that the CSQ-18B data provided clearer, more efficient, and more useful information for program planning and evaluation. In this study, service satisfaction measured by the CSQ-18B did not have any significant relationship to global or multidimensional (LDQ-30) measures of life satisfaction and well-being. In contrast, over a third of the variance in PSQ scores was accounted for by measures of life satisfaction. It appears that the PSQ elicits attitudes toward the more generalized health care delivery system as well as aspects of life satisfaction rather than reactions to specific services actually received.     

Adolescents’ Evaluations of Political Leaders: The Case of President Donald Trump

The current study explored youths’ views of President Donald Trump using quantitative ratings and open‐ended responses from a diverse sample of 1,432 U.S. adolescents from three geographic regions. Adolescent demographic characteristics (i.e., gender, race, geographic location) were systematically associated with their views about Trump. Open‐ended responses demonstrated substantial variability in youths’ rationales for approving or disapproving of the president. Adolescents' attitudes were informed by knowledge of the president’s leadership attributes, political views and policies, and their own experiences. Findings indicate that adolescents draw upon and synthesize a broad range of information when formulating their political views and coordinate this knowledge with their own opinions and experiences when evaluating political figures.     

Demonstrating LGBTQ+ Affirmative Practice in Groups:: Developing Competence through Simulation-Based Learning

Clinical Social Work Journal - Lesbian, gay, bisexual, transgender, queer, and other sexual and/or gender minority (LGBTQ+) populations experience significant mental and behavioral health...     

An approach to learning from both good and poor factory performance in a Kanban-based just-in-time production system

In a JIT manufacturing environment it may be desirable to learn from an archived history of data that contains information that reflects less than optimal factory performance. The purpose of this paper is to use rule induction to predict JIT factory performance from past data that reflects both poor (saturated or starved) and good (efficient) factory performance. Inductive learning techniques have previously been applied to JIT production systems (Markham et al. , Computers and Industrial Engineering, 34 , 717-726, 1998; Markham et al. , International Journal of Manufacturing Technology Management, 11 (4), 239-246, 2000), but these techniques were only applied to data sets that reflected a well-performing factory. This paper presents an approach based on inductive learning in a JIT manufacturing environment that (1) accurately classifies and predicts factory performance based on shop factors, and (2) identifies the important relationships between the shop factors that determine factory performance. An example application is presented in which the classification and regression tree (CART) technique is used to predict saturated, starved or efficient factory performance based on dynamic shop floor data. This means that the relationship between the variables that cause poor factory performance can be discovered and measures to assure efficient performance can then be taken.     

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.