Blinking While Speaking and Talking,Hearing, and Listening: Communication or Individual Underlying Process?

Journal of Nonverbal Behavior - Why do eye-blinks activate during conversation? We manipulated informational content and communicative intent exchanged within dyads. By comparison to a silent...     

Male victims of child sexual abuse

Despite the increased attention the subject of child sexual abuse has received in recent years, rarely has much consideration been given to the boys and adolescent males who are the victims of such abuse. That young girls are consistently the clear majority of sexual abuse victims is not disputed, however the extent to which they are the majority is in question. A review of existing literature has indicated that one of the leading factors for this inadvertent negligence in presenting a truer picture of the magnitude of the problem for male victims concerns the area of under-reporting of the incidence of abuse of young males. Under-reporting has served to give a false sense of the scope of child sexual abuse, particularly for male children. Many societal and methodological factors contribute to under-reporting. Reasons for the discrepancy in reporting abuse will be presented and implications for practice will be discussed.     

Text,competence and logic: An exercise

Professional medical practice, like other organizational conduct, relies upon records which document transactions between members and their clientele. Medical practitioners employ a set of conventions providing for the systematic recording and interpretation of medical record cards that forms a social organization underlying the records cards' ordinary usage. In this paper we examine these conventions and develop a computer program which captures elements of their structure and use. By doing so we illustrate one way in which sociological analysis can contribute to the design of intelligent systems. We also suggest that the emerging discipline of Artificial Intelligence might find recent developments in sociology pertinent to its concerns.     

The “wrong skewness” problem in stochastic frontier models: A new approach

Stochastic frontier models are widely used to measure, e.g., technical efficiencies of firms. The classical stochastic frontier model often suffers from the empirical artefact that the residuals of the production function may have a positive skewness, whereas a negative one is expected under the model, which leads to estimated full efficiencies of all firms. We propose a new approach to the problem by generalizing the distribution used for the inefficiency variable. This generalized stochastic frontier model allows the sample data to have the wrong skewness while estimating well-defined and nondegenerate efficiency measures. We discuss the statistical properties of the model, and we discuss a test for the symmetry of the error term (no inefficiency). We provide a simulation study to show that our model delivers estimators of efficiency with smaller bias than those of the classical model even if the population skewness has the correct sign. Finally, we apply the model to data of the U.S. textile industry for 1958–2005 and show that for a number of years our model suggests technical efficiencies well below the frontier while the classical one estimates no inefficiency in those years.     

A Review of Recent Advances in Benchmark Dose Methodology

In this review, recent methodological developments for the benchmark dose (BMD) methodology are summarized. Specifically, we introduce the advances for the main steps in BMD derivation: selecting the procedure for defining a BMD from a predefined benchmark response (BMR), setting a BMR, selecting a dose–response model, and estimating the corresponding BMD lower limit (BMDL). Although the last decade has shown major progress in the development of BMD methodology, there is still room for improvement. Remaining challenges are the implementation of new statistical methods in user‐friendly software and the lack of consensus about how to derive the BMDL.     

A Novel Approach to Chemical Mixture Risk Assessment—Linking Data from Population‐Based Epidemiology and Experimental Animal Tests

Humans are continuously exposed to chemicals with suspected or proven endocrine disrupting chemicals (EDCs). Risk management of EDCs presents a major unmet challenge because the available data for adverse health effects are generated by examining one compound at a time, whereas real‐life exposures are to mixtures of chemicals. In this work, we integrate epidemiological and experimental evidence toward a whole mixture strategy for risk assessment. To illustrate, we conduct the following four steps in a case study: (1) identification of single EDCs (“bad actors”)—measured in prenatal blood/urine in the SELMA study—that are associated with a shorter anogenital distance (AGD) in baby boys; (2) definition and construction of a “typical” mixture consisting of the “bad actors” identified in Step 1; (3) experimentally testing this mixture in an in vivo animal model to estimate a dose–response relationship and determine a point of departure (i.e., reference dose [RfD]) associated with an adverse health outcome; and (4) use a statistical measure of “sufficient similarity” to compare the experimental RfD (from Step 3) to the exposure measured in the human population and generate a “similar mixture risk indicator” (SMRI). The objective of this exercise is to generate a proof of concept for the systematic integration of epidemiological and experimental evidence with mixture risk assessment strategies. Using a whole mixture approach, we could find a higher rate of pregnant women under risk (13%) when comparing with the data from more traditional models of additivity (3%), or a compound‐by‐compound strategy (1.6%).