Sociologist as Dean and Dean as Sociologist

This article highlights contrasts between the identity and values of sociology faculty members versus the identity and values of college administrators. Some of these contrasts include: questioning authority and speaking truth to power versus being in a position of authority and engaging in political compromise; taking a critical, reflective, and independent stance as a scholar versus a dean taking a stance of positive attitude and setting direction in the organization; and acting in an egalitarian and democratic fashion versus acting in a discriminating manner and making executive decisions. This article also explores the ways in which being a sociologist can inform a dean’s understanding of the structure and organizational life of the college and the group dynamics among faculty, students, administrators, and other constituents.     

Contested canon: Simmel scholarship at Columbia and the new school

The history of sociology exhibits what might be called, after Gallie (1956), an “essentially contested “ canon. The key figures, sacred texts, and central ideas that constitute the sociological tradition are inherently in dispute. This essay examines the “contested canon “ within a historical framework to provide at least a partial explanation for the restricted interpretation of Georg Simmel as a structuralist sociologist. The sites of this contest are two New York City institutions, Columbia University and the New School for Social Research, both of which offered mid-century readings of Simmel’s works. At Columbia in the mid-1950s, Robert K. Merton advanced a structural reading of Simmel’s work. During the same broad period, the New School’s Albert Salomon championed a phenomenological reading of Simmel in his classes and seminars. Despite penetrating insights into Simmel’s links to the phenomenological tradition, Salomon’s interpretation has had less salience than the approach advanced by Merton. The differential success of these competing interpretations is explained in large measure by the institutionalization and dominance of Merton’s research tradition relative to Salomon’s.     

Development of a Single-Meal Fish Consumption Advisory for Methyl Mercury

Methyl mercury (meHg) contamination of fish is the leading cause of fish consumption advisories in the United States. These advisories have focused upon repeated or chronic exposure, whereas risks during pregnancy may also exist from a single-meal exposure if the fish tissue concentration is high enough. In this study, acute exposure to meHg from a single fish meal was analyzed by using the one-compartment meHg biokinetic model to predict maternal hair concentrations. These concentrations were evaluated against the mercury hair concentration corresponding to the U.S. Environmental Protection Agency's reference dose (RfD), which is intended to protect against neurodevelopmental effects. The one-compartment model was validated against blood concentrations from three datasets in which human subjects ingested meHg in fish, either as a single meal or multiple meals. Model simulations of the single-meal scenario at different fish meHg concentrations found that concentrations of 2.0 ppm or higher can be associated with maternal hair concentrations elevated above the RfD level for days to weeks during gestation. A single-meal fish concentration cutoff of > or = 2.0 ppm is an important consideration, especially because this single high exposure event might be in addition to a baseline meHg body burden from other types of fish consumption. This type of single-meal advisory requires that fish sampling programs provide data for individual rather than composited fish, and take into account seasonal differences that may exist in fish concentrations.     

Misunderstandings between experimentalists and observationalists about causal inference

Summary.  We attempt to clarify, and suggest how to avoid, several serious misunderstandings about and fallacies of causal inference. These issues concern some of the most fundamental advantages and disadvantages of each basic research design. Problems include improper use of hypothesis tests for covariate balance between the treated and control groups, and the consequences of using randomization, blocking before randomization and matching after assignment of treatment to achieve covariate balance. Applied researchers in a wide range of scientific disciplines seem to fall prey to one or more of these fallacies and as a result make suboptimal design or analysis choices. To clarify these points, we derive a new four-part decomposition of the key estimation errors in making causal inferences. We then show how this decomposition can help scholars from different experimental and observational research traditions to understand better each other's inferential problems and attempted solutions.