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  1. Theory Making from the Middle: Researching LGBTQ Communities in Small Cities

    Urban lesbian, gay, bisexual, transgender, and queer (LGBTQ) community research in sociology has largely ignored LGBTQ communities in the most common urban form: small cities. In this article, I argue that LGBTQ communities in small cities are an underexplored source of theory making about LGBTQ communities more broadly, and I highlight the ways such research enhances LGBTQ community research. I first discuss a definitional framework of LGBTQ communities in small cities. In other words, what do we mean by small cities, and what do we mean by LGBTQ communities within them?

  2. Small‐City Gay Bars, Big‐City Urbanism

    Despite the widely hailed importance of gay bars, what we know of them comes largely from the gayborhoods of four “great cities.” This paper explores the similarities of 55 lone small‐city gay bars to each other and the challenges they pose to the sexualities and urban literatures.

  3. Does Socio-structural Context Matter? A Multilevel Test of Sexual Minority Stigma and Depressive Symptoms in Four Asia-Pacific Countries

    In the Asia-Pacific region, individual sexual stigma contributes to elevated rates of depression among sexual minority men. Less well understood is the role of socio-structural sexual stigma despite evidence that social context influences the experience of stigma. We use data from the United Nations Multi-country Study on Men and Violence to conduct a multilevel test of associations between individual- and cluster unit–level indicators of sexual stigma and depressive symptoms among sexual minority men (n = 562).
  4. Computation and the Sociological Imagination

    Computational sociology leverages new tools and data sources to expand the scope and scale of sociological inquiry. It’s opening up an exciting frontier for sociologists of every stripe—from theorists and ethnographers to experimentalists and survey researchers. It expands the sociological imagination.

  5. Review Essay: See It with Figures

    The short story is that Kieran Healy’s Data Visualization: A Practical Introduction is a gentle introduction to the effective display of social science data using the R package ggplot2. It is beautifully put together, achingly clear, and effective.
  6. The Geometry of Culture: Analyzing the Meanings of Class through Word Embeddings

    We argue word embedding models are a useful tool for the study of culture using a historical analysis of shared understandings of social class as an empirical case. Word embeddings represent semantic relations between words as relationships between vectors in a high-dimensional space, specifying a relational model of meaning consistent with contemporary theories of culture.
  7. Queer Pop‐Ups: A Cultural Innovation in Urban Life

    Research on sexuality and space emphasizes geographic and institutional forms that are stable, established, and fixed. By narrowing their analytic gaze on such places, which include gayborhoods and bars, scholars use observations about changing public opinions, residential integration, and the closure of nighttime venues to conclude that queer urban and institutional life is in decline. We use queer pop‐up events to challenge these dominant arguments about urban sexualities and to advocate instead a “temporary turn” that analyzes the relationship between ephemerality and placemaking.

  8. Assessing Differences between Nested and Cross-Classified Hierarchical Models

    Sociological Methodology, Volume 49, Issue 1, Page 220-257, August 2019.
  9. Review Essay: Back to the Future

    In one of my undergraduate courses, I show students a photo of Paul Lazarsfeld and Frank Stanton. Of course, neither social scientist is familiar to them, but I argue to my students that Lazarsfeld had a bigger impact on the daily practice of sociology than any member of the Marx/Weber/Durkheim triumvirate they study in classical theory.

  10. A General Framework for Comparing Predictions and Marginal Effects across Models

    Many research questions involve comparing predictions or effects across multiple models. For example, it may be of interest whether an independent variable’s effect changes after adding variables to a model. Or, it could be important to compare a variable’s effect on different outcomes or across different types of models. When doing this, marginal effects are a useful method for quantifying effects because they are in the natural metric of the dependent variable and they avoid identification problems when comparing regression coefficients across logit and probit models.