Department of Sociology, UCLA

This paper engages abolition feminist storytelling as a methodology for feminist criminologists to identify, reflect on, grapple with, and ultimately confront carceral logics in the stories we (re)tell about violence and punishment. Drawing on over a decade’s worth of research, advocacy, and community organizing, we identify three carceral tensions that emerged within our own storytelling: the victim-offender binary, researcher complicity, and a survivor standpoint. Building on prior black feminist scholarship, we then illustrate how an abolition feminist approach to storytelling allows us to confront these tensions through relationality, an ethic of love, accountability, vulnerability, and commitment to justice.

Although policies against gender-based violence gained widespread acceptance in the twentieth century, seemingly global gender standards have not evenly diffused. Indeed, despite shared (post)socialist experiences, Ukraine has criminalized domestic violence (DV) while Russia has not. However, discursive analysis of 127 public texts published from 2011 to 2021 reveals that public talk on DV in both countries retains a similar genderless character—a surprising finding for Ukraine, which continues to harmonize with liberal European gender norms. Extending the work of Popova and Shevel, I argue that the “escalatory cycle” characterizing Russo–Ukrainian relations in the post-Soviet period can explain their divergent policies on DV, as well as their shared frames. This study highlights the limited salience of Western feminist frames in DV prevention efforts. It contributes to scholarship on norm diffusion and backlash, exploring how push–pull mechanisms and geopolitics guide the adoption and durability of ideas about gender.

We examine how new digital technologies are transforming labour processes in frontline jobs in United States (US) store‐based retail, examining the US as an extreme case of labour market liberalisation. Research on technological change presents three scenarios: job displacement, job enrichment and ‘digital Taylorism’ involving heightened surveillance and control. However, this research typically overlooks the role of customers and the frequency of technology failure. Drawing on interviews with frontline employees and managers supplemented by other sources, we find little evidence for job enhancement and limited evidence of job displacement. Retailers have used digital technologies to heighten Taylorisation, speedup and surveillance in some frontline jobs. However, interviews reveal multiple sources of chaos in the jobs, including unpredictable customer interactions, staffing patterns that make it hard for retail workers to rely on co‐workers, and flaws in digital technologies themselves. We summarise the current impact of digital technology adoption on US store‐based retail work as ‘chaotic rationalisation’—retail employers aim to use technology to rationalise work, but the result diverges from Taylorism because of chaotic elements in retail workplaces.

Causal explanation is central to sociological research, shaping both theoretical development and empirical inquiry. This paper argues that causal machine learning—which integrates deductive identification strategies with inductive estimation techniques—offers an analytical approach for modeling complex, nonlinear social processes within the potential outcomes framework. We argue that causal machine learning operates through an iterative feedback loop: Theoretical assumptions guide flexible estimation, which inductively uncovers complex heterogeneities and nonlinearities, and these discoveries subsequently refine and expand sociological knowledge. Drawing on a systematic review of recent sociological research (2014–2024), we highlight how causal machine learning is advancing work in three key areas: causal effect heterogeneity, causal mediation analysis, and time-varying causal inference. These developments expand the methodological tool kit available to sociologists and strengthen the discipline’s ability to test, refine, and extend theories of social explanation. We conclude by outlining emerging directions, including high-dimensional causal inference and generative artificial intelligence, that are opening new methodological frontiers in causal machine learning for sociology.

The Low-Income Housing Tax Credit (LIHTC) program relies on housing developers to build affordable housing units. Developers consider financial feasibility and programmatic regulations when planning projects, and one central project feature is the affordability of units. Decisions around unit affordability directly shape housing supply and, in turn, affect where low-income tenants live. In this article, I analyze LIHTC projects in California and show that, among projects funded from 2011 to 2023, only a small share (15%) of units was affordable to extremely low-income (ELI) households. In contrast, ELI households comprised the majority of LIHTC tenants. The share of units affordable to ELI households increased over time due in part to program regulations, financial feasibility, and state priorities around housing formerly homeless individuals, though there was still a substantial mismatch between units’ affordability and tenants’ incomes during this period. Units affordable to ELI households are slightly less likely to be in the highest socioeconomic status (SES) communities, though mismatch between affordability and tenant income is similar across neighborhood types. Developer decision-making around income targeting, tenant type, and project location shapes affordable housing supply, and I conclude by noting the importance of assessing the intersections of these factors for future policymaking.

Social relationships play a crucial role in shaping health. To better understand the underlying mechanisms, we explored the independent and interactive effects of perceived emotional support (PES) and marital status on body mass index (BMI), eating behaviors, brain reactivity to food images, plasma oxytocin, and alterations in the brain-gut microbiome (BGM) system. Brain responses to food stimuli, fecal metabolites, and plasma oxytocin levels were measured in 94 participants. Structural equation modeling was used to determine the integrated pathways linking social factors to obesity-related outcomes. Marital status and PES interact and independently influence lower BMI, healthier eating behaviors, increased oxytocin levels, food-cue reactivity in frontal brain regions involved in craving inhibition and executive control, and tryptophan-pathway metabolites related to inflammation, immune regulation, and energy homeostasis. These findings suggest that supportive human relationships, particularly high-quality marital bonds, may regulate obesity risk through oxytocin-mediated alterations in brain and gut pathways.

BACKGROUND AND OBJECTIVES: Multimorbidity (2+ chronic conditions) associated with faster cognitive decline among older adults, yet longitudinal evidence from low- and middle-income countries, including Mexico, remains limited. This study examines cohort differences in the annual rate of cognitive decline, measured by global cognitive function scores (GCFS), and tests whether the association between multimorbidity and cognitive decline differs between two cohorts aged 50-60 in 2001 and 2012. RESEARCH DESIGN AND METHODS: We assess two 10-year birth cohorts (Cohort 1: born 1941-1951, n = 5,345 Cohort 2: born 1952-1962, n = 4,378), at 3 time points (Cohort 1: 2001, 2003, and 2012; Cohort 2: 2012, 2015, 2021), at ages 50-60 at baseline. We examine cohort differences in average annual GCFS changes by fitting growth curve models incorporating random intercepts and slopes. RESULTS: Two key findings emerged. First, the earlier cohort (Cohort 1, 2001), experienced a faster average annual rate of decline in GCFS than the recent cohort (Cohort 2, 2012). Second, the link between multimorbidity and cognitive decline did not significantly differ between cohorts net of possible confounders. DISCUSSION AND IMPLICATIONS: Our findings advance our understanding of cohort differences in cognitive decline and how the influence of multimorbidity on cognitive decline has evolved in Mexico. The slower rate of decline among the recent cohort suggests potential improvements in cognitive reserve due to educational improvements. Improvements in healthcare access over the past decades may have mitigated the negative consequences of multimorbidity on cognitive decline, potentially explaining the absence of cohort differences.

This rejoinder traces the origin of segmented assimilation theory and addresses the critical comments by highlighting some key points in the evolution of the concept and empirical research from the early nineteen nineties to the present.