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  1. Invisible Inequality Among “Wounded Warriors”

    The term “wounded warriors,” both a socially designated status and an official medical classification, creates divisions among service members.

  2. When the Personal is Political—and Infectious

    Privilege, distrust, individual choice, and parental care all factor into vaccine resistance, but the consequences are anything but personal.

  3. The Struggle to Save Abortion Care

    by Carole Joffe, Summer 2018 Contexts

  4. The Emergence of Statistical Objectivity: Changing Ideas of Epistemic Vice and Virtue in Science

    The meaning of objectivity in any specific setting reflects historically situated understandings of both science and self. Recently, various scientific fields have confronted growing mistrust about the replicability of findings, and statistical techniques have been deployed to articulate a “crisis of false positives.” In response, epistemic activists have invoked a decidedly economic understanding of scientists’ selves. This has prompted a scientific social movement of proposed reforms, including regulating disclosure of “backstage” research details and enhancing incentives for replication.
  5. Mechanisms Linking Social Ties and Support to Physical and Mental Health

    Over the past 30 years investigators have called repeatedly for research on the mechanisms through which social relationships and social support improve physical and psychological well-being, both directly and as stress buffers. I describe seven possible mechanisms: social influence/social comparison, social control, role-based purpose and meaning (mattering), self-esteem, sense of control, belonging and companionship, and perceived support availability. Stress-buffering processes also involve these mechanisms.

  6. Understanding Racial-ethnic Disparities in Health: Sociological Contributions

    This article provides an overview of the contribution of sociologists to the study of racial and ethnic inequalities in health in the United States. It argues that sociologists have made four principal contributions. First, they have challenged and problematized the biological understanding of race. Second, they have emphasized the primacy of social structure and context as determinants of racial differences in disease. Third, they have contributed to our understanding of the multiple ways in which racism affects health.

  7. From the Bookshelf of a Sociologist of Diagnosis: A Review Essay

    The present essay will take readers through the bookshelf of this sociologist of diagnosis. It will demonstrate the wide-reaching topics that I consider relevant to the sociologist who considers diagnosis as a social object and also as a point of convergence where doctor and lay person encounter one another, where authority is exercised, health care is organized, political priorities are established, and conflict is enacted.

  8. The Problem of Underdetermination in Model Selection

    Conventional model selection evaluates models on their ability to represent data accurately, ignoring their dependence on theoretical and methodological assumptions. Drawing on the concept of underdetermination from the philosophy of science, the author argues that uncritical use of methodological assumptions can pose a problem for effective inference. By ignoring the plausibility of assumptions, existing techniques select models that are poor representations of theory and are thus suboptimal for inference.
  9. Comment: The Inferential Information Criterion from a Bayesian Point of View

    As Michael Schultz notes in his very interesting paper (this volume, pp. 52–87), standard model selection criteria, such as the Akaike information criterion (AIC; Akaike 1974), the Bayesian information criterion (BIC; Schwarz 1978), and the minimum description length principle (MDL; Rissanen 1978), are purely empirical criteria in the sense that the score a model receives does not depend on how well the model coheres with background theory. This is unsatisfying because we would like our models to be theoretically plausible, not just empirically successful.
  10. Causal Inference with Networked Treatment Diffusion

    Treatment interference (i.e., one unit’s potential outcomes depend on other units’ treatment) is prevalent in social settings. Ignoring treatment interference can lead to biased estimates of treatment effects and incorrect statistical inferences. Some recent studies have started to incorporate treatment interference into causal inference. But treatment interference is often assumed to follow a simple structure (e.g., treatment interference exists only within groups) or measured in a simplistic way (e.g., only based on the number of treated friends).