Conflict of Interest Mitigation Procedures May Have Little Influence on the Perceived Procedural Fairness of Risk‐Related Research

Two between‐subject experiments explored perceived conflict of interest (COI)—operationalized as perceived procedural unfairness—in a hypothetical public–private research partnership to study the health risks of trans fats. Perceived fairness was measured as subjects’ perceptions that health researchers would be willing to listen to a range of voices and minimize bias (i.e., COI) in the context of a research project. Experiment 1 (n = 1,263) randomly assigned research subjects to a partnership that included (1) a combination of an industry partner, a university partner, and a nongovernmental organization (NGO) partner; and (2) one of three processes aimed at mitigating the potential for COI to harm the quality of the research. The procedures included an arm's‐length process meant to keep the university‐based research team from being influenced by the other partners, an independent advisory board to oversee the project, and a commitment to making all data and analyses openly available. The results suggest that having an industry partner has substantial negative effects on perceived fairness and that the benefit of employing a single COI‐mitigation process may be relatively small. Experiment 2 (n = 1,076) assessed a partnership of (1) a university and either an NGO or industry partner and (b) zero, one, two, or three of the three COI‐mitigation procedures. Results suggest there is little value in combining COI‐mitigation procedures. The study has implications for those who aim to foster confidence in scientific findings for which the underlying research may benefit from industry funding.     

Quantifying Water Pathogen Risk in an Epidemiological Framework

Traditionally, microbial risk assessors have used point estimates to evaluate the probability that an individual will become infected. We developed a quantitative approach that shifts the risk characterization perspective from point estimate to distributional estimate, and from individual to population. To this end, we first designed and implemented a dynamic model that tracks traditional epidemiological variables such as the number of susceptible, infected, diseased, and immune, and environmental variables such as pathogen density. Second, we used a simulation methodology that explicitly acknowledges the uncertainty and variability associated with the data. Specifically, the approach consists of assigning probability distributions to each parameter, sampling from these distributions for Monte Carlo simulations, and using a binary classification to assess the output of each simulation. A case study is presented that explores the uncertainties in assessing the risk of giardiasis when swimming in a recreational impoundment using reclaimed water. Using literature-based information to assign parameters ranges, our analysis demonstrated that the parameter describing the shedding of pathogens by infected swimmers was the factor that contributed most to the uncertainty in risk. The importance of other parameters was dependent on reducing the a priori range of this shedding parameter. By constraining the shedding parameter to its lower subrange, treatment efficiency was the parameter most important in predicting whether a simulation resulted in prevalences above or below non outbreak levels. Whereas parameters associated with human exposure were important when the shedding parameter was constrained to a higher subrange. This Monte Carlo simulation technique identified conditions in which outbreaks and/or nonoutbreaks are likely and identified the parameters that most contributed to the uncertainty associated with a risk prediction.     

Equality and Illusion: Gender and Tenure in Art History Careers

Using a national survey of 508 art history Ph.D.s including data on graduate school performance and careers 10 – 15 years post‐Ph.D., this study investigates gender, family, and academic tenure in art history, the humanities field with the highest proportion of women. Alternative hypotheses derived from three perspectives—termed here clockwork, two‐body, and synergy—are evaluated with multivariate logistic regression. Analysis finds that marriage increases men’s tenure odds and decreases women’s, but that some types of marriages do not decrease women’s odds, and some types dramatically increase men’s. This study calls attention to male advantage in female‐dominated academic disciplines and demonstrates the potential to better understand the interactions of gender, marriage, and careers by conceptualizing different types of marriages.     

Difference,subjectivities and power: (de)colonizing practices in internationalizing the curriculum

This paper problematizes the discourse of internationalizing the curriculum using a critical framework of difference. The author draws on her transnational experiences as an international postgraduate researcher in an Australian university, an educator in Malaysia and her present experiences as an academic in an Australian university. The notions of difference, power and subjectivities are used to unpack the academic and pedagogical practices of internationalizing the curriculum. A politics of difference in internationalizing the curriculum entails interrogating the power dynamics in the privileging and marginalization of knowledge, skills and ways of being, knowing and doing. Transnational connectivity and reciprocal flows of individuals and knowledge are emphasized within such practices.     

Bootstrap adaptive estimation: The trimmed-mean example

We consider the problem of choosing among a class of possible estimators by selecting the estimator with the smallest bootstrap estimate of finite sample variance. This is an alternative to using cross-validation to choose an estimator adaptively. The problem of a confidence interval based on such an adaptive estimator is considered. We illustrate the ideas by applying the method to the problem of choosing the trimming proportion of an adaptive trimmed mean. It is shown that a bootstrap adaptive trimmed mean is asymptotically normal with an asymptotic variance equal to the smallest among trimmed means. The asymptotic coverage probability of a bootstrap confidence interval based on such adaptive estimators is shown to have the nominal level. The intervals based on the asymptotic normality of the estimator share the same asymptotic result, but have poor small-sample properties compared to the bootstrap intervals. A small-sample simulation demonstrates that bootstrap adaptive trimmed means adapt themselves rather well even for samples of size 10.     

A conceptual analysis of request teaching procedures for individuals with severely limited verbal repertoires

The Analysis of Verbal Behavior - There have been many published reports of attempts to teach requests to individuals with severely limited verbal repertoires associated with developmental...     

Laplace approximations to posterior moments and marginal distributions on circles,spheres, and cylinders

We extend recent work on Laplace approximations (Tierney and Kadane 1986; Tierney, Kass, and Kadane 1989) from parameter spaces that are subspaces of R^k to those that are on circles, spheres, and cylinders. While such distributions can be mapped onto the real line (for example, a distribution on the circle can be thought of as a function of an angle θ, 0 ? 0 ? 2π), that the end points coincide is not a feature of the real line, and requires special treatment. Laplace approximations on the real line make essential use of the normal integral in both the numerator and the denominator. Here that role is played by the von Mises integral on the circle, by the Bingham integrals on the spheres and hyperspheres, and by the normal-von Mises and normal-Bingham integrals on the cylinders and hypercylinders, respectively. We begin with a brief introduction to Laplace approximations and to previous Bayesian work on circles, spheres, and cylinders. We then develop the theory for parameter spaces that are hypercylinders, since all other shapes considered here are special cases. We compute some examples, which show reasonable accuracy even for small samples.