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  1. Neighborhood Diversity and the Rise of Artist Hotspots: Exploring the Creative Class Thesis Through a Neighborhood Change Lens

    The diversity of the U.S. urban population has increased dramatically in recent decades, yet the processes through which population diversity may be driving neighborhood change remain insufficiently understood. Building on Claude Fischer's subcultural theory of urbanism and other classic sociological insights, this article makes the case that population diversity shapes the character of place and drives the spatial clustering of artists and art organizations.

  2. Do‐It‐Yourself Urban Design: The Social Practice of Informal “Improvement” Through Unauthorized Alteration

    There are numerous ways in which people make illegal or unauthorized alterations to urban space.

  3. The Relevance of Organizational Sociology

    Brayden G. King reviews Manufacturing Morals: The Values of Silence in Business School Education by Michel Anteby, Hyper-Organization: Global Organizational Expansion by Patricia Bromley and John W. Meyer, The Vanishing American Corporation: Navigating the Hazards of a New Economy by Gerald F. Davis and The Fracturing of the American Corporate Elite by Mark S. Mizruchi. 

  4. Neoliberalism

    Johanna Bockman unpacks a hefty term, neoliberalism. She cites its roots and its uses, decoding it as a description of a “bootstraps” ideology that trumpets individualism and opportunity but enforces conformity and ignores structural constraints.

  5. Childhood Family Instability and Young Adult Health

    American children live in a variety of family structures throughout their childhoods. Such instability in family arrangements is common and has important demonstrated implications for short-term child outcomes. However, it is not known whether family instability experienced in childhood has enduring health consequences across the life course.
  6. Estimating the Relationship between Time-varying Covariates and Trajectories: The Sequence Analysis Multistate Model Procedure

    The relationship between processes and time-varying covariates is of central theoretical interest in addressing many social science research questions. On the one hand, event history analysis (EHA) has been the chosen method to study these kinds of relationships when the outcomes can be meaningfully specified as simple instantaneous events or transitions.
  7. 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.
  8. Comment: Evidence, Plausibility, and Model Selection

    In his article, Michael Schultz examines the practice of model selection in sociological research. Model selection is often carried out by means of classical hypothesis tests. A fundamental problem with this practice is that these tests do not give a measure of evidence. For example, if we test the null hypothesis β = 0 against the alternative hypothesis β ≠ 0, what is the largest p value that can be regarded as strong evidence against the null hypothesis? What is the largest p value that can be regarded as any kind of evidence against the null hypothesis?
  9. Comment: Bayes, Model Uncertainty, and Learning from Data

    The problem of model uncertainty is a fundamental applied challenge in quantitative sociology. The authors’ language of false positives is reminiscent of Bonferroni adjustments and the frequentist analysis of multiple independent comparisons, but the distinct problem of model uncertainty has been fully formalized from a Bayesian perspective.
  10. Comment: Some Challenges When Estimating the Impact of Model Uncertainty on Coefficient Instability

    I once had a colleague who knew that inequality was related to an important dependent variable. This colleague knew many other things, but I focus on inequality as an example. It was difficult for my colleague to know just how to operationalize inequality. Should it be the percentage of income held by the top 10 percent, top 5 percent, or top 1 percent of the population? Should it be based on the ratio of median black income to median white income, or should it be the log of that ratio? Should it be based on the Gini index, or perhaps the Theil index would be better?