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  1. Gentrification in Three Paradoxes

    As recently as 2006, students walked into the first day of my urban sociology course at Brooklyn College without knowing the term “gentrification.” Within a few years, however, even students who lived in neighborhoods that seemed unlikely candidates were claiming that their area was undergoing this transformation. Many point to new residents who are close to their age but visibly different: artists, students, or “hipsters” living on their own rather than with their families, and mostly white.

  2. Parks for Profit: The High Line, Growth Machines, and the Uneven Development of Urban Public Spaces

    This paper investigates the growing inequality of public spaces in contemporary cities. In the era of neoliberal urbanism, stratified economic and cultural resources produce a spectrum of unevenly developed public parks, ranging from elite, privatized public spaces in wealthy districts to neglected parks in poor neighborhoods.

  3. Cities and the Creative Class

    Cities and regions have long captured the imagination of sociologists, economists, and urbanists. From Alfred Marshall to Robert Park and Jane Jacobs, cities have been seen as cauldrons of diversity and difference and as fonts for creativity and innovation. Yet until recently, social scientists concerned with regional growth and development have focused mainly on the role of firms in cities, and particularly on how these firms make location decisions and to what extent they concentrate together in agglomerations or clusters.

  4. Review Essays: Ivory Tower Fantasies about Affirmative Actions

    In a provocative 2002 essay, political scientist Jennifer Hochschild asks: why has affirmative action been so central to the American culture wars, more so than wage discrimination, underfunded public schools, and a litany of other social issues that have far greater impact on more black Americans?1 And why have social scientists paid affirmative action so little empirical attention, in contrast to the deep philosophical and legal thinking on the topic?
  5. 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.

  6. Estimating Income Statistics from Grouped Data: Mean-constrained Integration over Brackets

    Researchers studying income inequality, economic segregation, and other subjects must often rely on grouped data—that is, data in which thousands or millions of observations have been reduced to counts of units by specified income brackets.
  7. Limitations of Design-based Causal Inference and A/B Testing under Arbitrary and Network Interference

    Randomized experiments on a network often involve interference between connected units, namely, a situation in which an individual’s treatment can affect the response of another individual. Current approaches to deal with interference, in theory and in practice, often make restrictive assumptions on its structure—for instance, assuming that interference is local—even when using otherwise nonparametric inference strategies.
  8. 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.
  9. 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?
  10. 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.