I checked 9 sociology journals on Saturday, June 06, 2026 using the Crossref API. For the period May 30 to June 05, I found 20 new paper(s) in 7 journal(s).

American Journal of Sociology

Networks at Work: Officer Diversity, Network Segregation, and Police Misconduct
Linda Zhao
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Social Forces

Review of “Traders, Speculators, and Captains of Industry: How Capitalist Legitimacy Shaped Foreign Investment Policy in India”
Manjusha Nair
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The code of cohesion: adolescent network centrality, offending, and the downside of school cohesion
Nicolo P Pinchak
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Paradoxical to numerous theories and policies, adolescents attending schools high in student cohesion do not reliably exhibit less involvement in offending. Coleman’s arguments about status and norms and the broader literature on adolescent networks suggest that this paradox is partially attributable to high-status males having more leeway to offend when attending schools high in student cohesion, which over time reinforces their engagement in offending. Here, an initial test of this “code of cohesion” hypothesis is conducted using data from high school students in Waves I–III of the National Longitudinal Study of Adolescent to Adult Health. Adolescents’ status positions at school are identified using multiple measures of network centrality, and high-cohesion schools are identified as those high on measures of student friendship network density or school social climate. Consistent with the hypothesis, results indicate that the positive association of male adolescents’ network centrality with offending becomes increasingly evident over time when they attend high-cohesion schools and diminishes over time after exiting these schools. Among schools of low/moderate-cohesion, the association of males’ network centrality with offending is less evident and does not persist after exiting schooling whatsoever. These findings support Coleman’s arguments about status and norms enabling leeway and illuminate a key reason why efforts to reduce offending by fostering student cohesion have had limited success. Additionally, this study highlights the need for research assessing how school relational dynamics among teachers, administrators, and parents, in addition to adolescent social systems, shape adolescents’ behavioral trajectories.
Review of “Shadows of the Enlightenment: The Hidden Politics and Ideology of the Natural and Social Sciences”
Michael Roberts
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Review of “Gray Is Beautiful: Confronting the Retreat of Democracy from the Radical Center”
Tad Skotnicki
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Bad timing: mass-participatory asset bubbles as a mechanism of predatory inclusion
Adam Goldstein, Max Fineman
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This paper considers the relationship between market composition and temporal dynamics of ethno-racial stratification in mass-participatory asset bubbles. Beginning from the insight that bubbles possess the social structure of a Ponzi scheme, we first ask to what extent timing of entry is structured by racial status and associated resource disparities among participants. We then consider to what extent homophilous social cues and segregation amplify disparate rates of late-stage entry. We focus on the 2000–2008 US residential housing market, using neighborhood-level data from the Home Mortgage Disclosure Act and the Federal Housing Finance Authority. Conditional on mortgage lending terms, credit availability, and other tract-level characteristics, trends in housing purchase rates for Blacks and Hispanics diverged from those of Whites as prices neared their peaks. Residential segregation and processes of homophilous diffusion amplify price-bidding and exacerbate racial disparities in rates of late-stage entry by rendering actors less attuned to the emergent risks of buying at elevated prices, and more vulnerable to the overtures of predatory agents. The analysis links the sociology of financial markets to studies of racialized predatory inclusion by considering heterogenous market timing, a mechanism which has become increasingly salient as financialization draws more diverse actors into volatile asset markets.
Review of “Is Inequality the Problem?”
Christopher Wimer
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Social Networks

Stability of egocentric social networks in children: Personal and functional dynamics across microsystems
Karl Titze, Christian Elting, Roswitha Sommer-Himmel
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Wiretap surveillance of criminal networks does not resemble other types of collaborative networks
Mitch Macdonald
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Estimating within-cluster and between-cluster spillover effects in randomized saturation designs
Sizhu Lu, Lei Shi, Peng Ding
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Social Science Research

The good, the bad, and the healthy: A factorial survey analysis of situational morality in consumption and healthcare
Selen GĂĽler
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Russia’s invasion of Ukraine and perceived intergenerational mobility in Czechia and Uruguay. An unexpected event during survey design study
Maik Hamjediers, Patrick Präg, Alexi Gugushvili
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On your mark, get set, go: A multilevel analysis of racial residential segregation in U.S. metropolitan areas
Nereyda Y. Ortiz Osejo, Amber R. Crowell, Luna Chandna
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Sociological Methods & Research

Discovering Preference Structure Using Randomized Paired Comparisons in Surveys: A Topic Modeling Approach
Jeong-han Kang, Eunrang Kwon, Junmo Song
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Randomized paired comparisons (RPC) for social values have various advantages over a matrix format of multiple items; however, their use cannot exhaust all possible pairs if there are too many items to compare one-to-one. This article proposes (1) applying a dimension reduction method, structural topic modeling (STM), to RPC survey data by restructuring answers into ordered pairs to estimate latent answering patterns, (2) visualizing them into directed graphs, and (3) interpreting them as respondents’ preference structures among social values. For empirical validation, we randomly divided 920 respondents into RPC and matrix-format groups and asked about the seriousness of ten social problems. Our STM from the RPC group revealed five preference structures beyond a linear order among the 10 items, which are interpretable and incorporate statistical tests with respondents’ traits as covariates. We also discuss how to improve topic modeling with RPC and contribute to various research streams, such as cultural value networks and gamification, by pairwise wiki survey.
Identification and Sensitivity Analysis for Teacher Bias Designs Based on Administrative Data
Julian Schuessler
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A series of papers uses administrative data on school students’ grades to assess whether teachers discriminate against certain demographic groups. Often, differences in teacher and test grades are regressed on student-level variables. However, it is unclear under what circumstances such an estimation strategy is valid. We conceptualize teacher bias as a direct causal effect of student-level attributes on teacher grades, fixing student ability. Standardized tests merely proxy for student ability; additionally, there may be confounders of ability and teacher grade. Accordingly, teacher bias is nonparametrically unidentified. However, we suggest substantive and parametric assumptions that ensure identification using difference-in-grades estimators. Estimators based on regression control for test grades are shown to be inconsistent even under these strong assumptions. We then develop a parametric sensitivity analysis that allows researchers to investigate the consequences of departures from critical assumptions. We illustrate our methodology using administrative data from Denmark.

Sociological Science

Declining Inequality and Persistent Inequality Structures
Soohyun Roh, Nathan Wilmers
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Family Networks and Childcare Choices: A Predictive Machine Learning Approach
Nicolás Soler, Tom Emery, Agnieszka Kanas
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Socius

The Self-Other Gap in Perceived Automation Risk: Evidence from the United States and Canada
Paul Glavin, Scott Schieman, Alexander Wilson
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This visualization shows a systematic misperception in how workers judge automation risk. Drawing on the 2026 Measuring Employment Sentiments and Social Inequality study, the authors compare paired measures of perceived automation likelihood for self and most others, using nationally representative samples of American and Canadian workers. Approximately three quarters of study participants in both countries rated their own jobs as at low risk of automation in the next few years, yet more than 70 percent believed that most other workers face at least some likelihood of automation. The pattern aligns with pluralistic ignorance: most workers hold one view of their own automation risk while assuming that most others hold a different one. The self-other gap is invariant across occupational categories and across two distinct national contexts, consistent with an informational asymmetry in which beliefs about others’ risk reflect prevailing public narratives about artificial intelligence rather than workers’ direct experience.
Resource Stratification and Income Segregation in Brazilian and Chilean Universities
Danilo Kuzmanic
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Wealthy universities enjoy advantages in accessing resources, disproportionately benefiting high-income students who are overrepresented in these institutions. Using administrative data from 2017 to 2019, the author analyzes the relationship between resource inequality and student income segregation across universities in Brazil and Chile. Both countries have significantly expanded higher education through the entry of new private institutions while introducing large-scale equity policies. The findings show comparable levels of resource inequality in both countries but greater student segregation in Chile. As a result, high-income students in Chile attend universities with 1.7 times more resources than their lower income peers compared with 1.3 times in Brazil. The public sector exhibits larger resource disparities in both countries, which is closely connected to differences in research intensity. High-income students are concentrated at well-funded public institutions that conduct most of these countries’ research, while public universities serving lower income students are more likely to be underfunded. In contrast, research disparities play a more minor role in the private sector: private universities targeting high-income niches access more resources regardless of their research capacity. The concentration of resources in a few institutions that primarily enroll high-income students is likely to lessen the equity implications of increasing participation in higher education.
Group Differences in Income Distributions, Poverty Gaps, and Poverty Buffers: Inequalities between the Children of Swedish-Born and Migrant Parents
Siddartha Aradhya, Raffaele Grotti, Rense Nieuwenhuis
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Conventional approaches define income poverty as a binary status, implying that being poor or nonpoor is equivalent across groups. The authors propose a novel visualization that moves beyond head count rates to display group-specific income distributions relative to the poverty threshold, simultaneously illustrating poverty prevalence, poverty gaps, and what the authors term poverty buffers: the distance above the threshold capturing income security. The authors apply this approach to all children aged 0 to 18 years in Sweden in 2022, by mother’s country of birth. The visualization reveals three patterns obscured by standard indicators. First, head count poverty rates vary dramatically, from 8 percent to 77 percent across groups. Second, average poverty gaps are surprisingly similar despite vast differences in prevalence, reflecting stark stratification by parental migration background. Third, poverty buffers reveal cumulative advantage: lower poverty rates coincide with substantially greater income security. By rendering gaps, buffers, and full distributions in a single figure, this visualization exposes dimensions of inequality that no single poverty measure captures alone.