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  1. Understanding the Crime Gap: Violence and Inequality in an American City

    The United States has experienced an unprecedented decline in violent crime over the last two decades. Throughout this decline, however, violent crime continued to concentrate in socially and economically disadvantaged urban neighborhoods. Using detailed homicide records from 1990 to 2010, this study examines the spatial patterning of violent crime in Chicago to determine whether or not all neighborhoods experienced decreases in violence.

  2. Community and Crime: Now More than Ever

    To introduce City & Community's symposium on “Community and Crime,” we describe the core connections between urban/community sociology and criminology, highlight the shared history of our scholarly traditions and missions, argue for a more collaborative future, and identify priorities for future research.

  3. Coleman’s Boat Revisited: Causal Sequences and the Micro-macro Link

    This article argues that empirical social scientists can be freed from having to account for “micro-to-macro transitions.” The article shows, in opposition to the (still) dominant perspective based on Coleman’s macro-micro-macro model, that no micro-macro transitions or mechanisms connect the individual level to the macro level in empirical social science. Rather, when considering that social macro entities and properties are micro manifest rather than macro manifest, it becomes clear that the micro-macro move in empirical social science is purely conceptual or analytical.
  4. Visualizing Stochastic Actor-based Model Microsteps

    This visualization provides a dynamic representation of the microsteps involved in modeling network and behavior change with a stochastic actor-based model. This video illustrates how (1) observed time is broken up into a series of simulated microsteps and (2) these microsteps serve as the opportunity for actors to change their network ties or behavior. The example model comes from a widely used tutorial, and we provide code to allow for adapting the visualization to one’s own model.

  5. Response to Morgan: On the Role of Status Threat and Material Interests in the 2016 Election

    I am delighted to have the opportunity to respond to Morgan’s article, which is a critique of my recent publication (Mutz 2018). I will restrict my response to matters concerning the data and analysis, excluding issues such as whether the journal PNAS is appropriately named (Morgan this issue:3) as well as Morgan’s views about how this work was covered in various media outlets (Morgan this issue:3–6). These issues are less important than whether material self-interest or status threat motivated Trump supporters.

  6. Correct Interpretations of Fixed-effects Models, Specification Decisions, and Self-reports of Intended Votes: A Response to Mutz

    The author thanks Professor Mutz for her informative reaction to his article. In this six-part response, the author first addresses Professor Mutz’s new claim that “Morgan’s interpretation suggests a misunderstanding of the panel models.” The author explains that this concern with his understanding can be set aside because Mutz’s interpretations of her own fixed-effects models are incorrect.

  7. Correction

    In the trends piece, “Taking a Knee” (Summer 2018), two figures had labeling errors. Please see with corrected figures here or visit contexts.org/articles/nfl for the full article with corrections.
  8. Letter to the Editors

    Timothy M. Gill writes to add context to the Summer 2018 issue’s policy brief and urge an interrogation of assumptions that democracy assistance is a benign form of foreign policy.
  9. The Rise of Ethnoburbs

    Samuel Hoon Kye on Asian American enclaves and ethnoburbs.
  10. The Structure of Causal Chains

    Sociologists are increasingly attentive to the mechanisms responsible for cause-and-effect relationships in the social world. But an aspect of mechanistic causality has not been sufficiently considered. It is well recognized that most phenomena of interest to social science result from multiple mechanisms operating in sequence. However, causal chains—sequentially linked mechanisms and their enabling background conditions—vary not just substantively, by the kind of causal work they do, but also structurally, by their formal properties.