Lawyers keep the gates of public justice institutions, particularly through their roles in formal procedures like hearings and trials. Yet, it is not clear what lawyers do in such quintessentially legal settings: conclusions from past research are bedeviled by a lack of clear theory and inconsistencies in research design. Conceptualizing litigation work in terms of professional expertise, I conduct a theoretically grounded synthesis of the findings of extant studies of lawyers’ impact on civil case outcomes.
American Indians experience forms of domination and resist them through a wide range of decolonizing processes that are commonly overlooked, misidentified, or minimally analyzed by American sociology. This inattention reflects the naturalizing use of minoritizing frameworks regarding tribal members and ethnic rather than political conceptions of American Indian nationhood, membership, and identity.
I critique Glenn’s article on settler colonialism and Bonilla-Silva’s article on critical race analysis from Indigenous perspectives, including racial genocide and world-systems analysis, to cover five centuries of global systemic racism during the conquest of the Americas, by Spanish and English colonizers and United States imperialism. I also propose macro-structural, comparative-historical analysis of racism including the destruction, resistance, and revitalization of Native Nations and American Indians.
ASA speaks with sociologist Doug Hartmann at the 2016 ASA Annual Meeting on August, 2016, in Seattle, WA. Hartmann talks about what it means to “do sociology,” how he uses sociology in his work, highlights of his work in the field, the relevance of sociological work to society, and his advice to students interested in entering the field.
On the Klamath River in northern California, Karuk tribal fishermen traditionally provide salmon for food and ceremonies, yet the region has sustained serious environmental degradation in recent years. What happens to Karuk masculinity when there are no fish? Using interviews and public testimony, the authors examine how declining salmon runs affect the gender identities and practices of Karuk fishermen. Gendered practices associated with fishing serve ecological functions, perpetuate culture in the face of structural genocide, and unite families and communities.
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.
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.
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.
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.