Interviewing academics about contested knowledge presents significant methodological challenges, as the interview situation often activates “official” professional repertoires that conceal ambivalence and the active construction of epistemic boundaries. To address these limitations, the authors propose and evaluate the confrontational-reflexive interview model (CRIM), a research design aimed at systematically eliciting reflexivity and analyzing boundary-work in situ . The model treats the interview not as a neutral information extraction tool but as a structured sequence of meaning-making occasions. Stage 1 establishes a baseline narrative in expert interviews and focus groups, which is subsequently disrupted by a standardized “confrontation stimulus”—a scholarly text explicitly challenging the dominant disciplinary consensus—forcing participants to negotiate their position against a credible internal critique. Stage 2, conducted approximately one month later via asynchronous written correspondence, introduces temporal distance to test the stability and revisability of these accounts outside the immediate pressure of the face-to-face interaction. Drawing on a study of political scientists’ discourse on conspiracy theories, the authors demonstrate that although standard interviewer probes often fail to unsettle “official” narratives, the combination of a third-party textual confrontation and delayed reflection successfully reveals the specific conditions under which experts reproduce, repair, or redraw the boundaries of legitimate knowledge. The authors argue that CRIM contributes to sociological methodology by offering a replicable framework for observing expertise as a contingent, interactional performance.
Causal inference approaches often emphasize binary treatments. But in many applications, the underlying constructs are continuous. In the potential outcomes framework, a continuous treatment can take on numerous values, each corresponding to a potential outcome that may be realized. In this setting, common estimands may be intractable because of a common issue in social research, particularly research on social inequality: the exposure is highly stratified by confounders. The authors show how to avoid drawing inferences about counterfactuals where data are unlikely to exist by carefully selecting the causal estimand. The authors adopt an additive shift estimand that adds a small, fixed amount to each unit’s income. This approach is preferable to population-average dose-response curves in settings in which some treatment values rarely occur in some subgroups. The authors also show how to estimate and summarize patterns of nonlinearity and effect heterogeneity with continuous treatments. As a motivating example, the authors consider the causal effect of parental income on college attendance, a setting in which the exposure is highly stratified by confounders (e.g., parental education). This approach applies to a wide range of possible treatment conditions in sociology.
In the thriving field of network studies, there has been an emerging practice of comparing optimal modularity across social networks to evaluate the variation of network module-related substantive concepts, such as the level of consensus, polarization, or community boundary rigidity. This practice offers valuable insights and is often thoughtfully motivated, but it faces several conceptual and empirical challenges that merit careful consideration. Conceptually, the selected modularity metric may misalign with the substantive concepts researchers aim to measure. Empirically, estimated optimal modularity is highly sensitive to algorithm choice and to network characteristics unrelated to those substantive concepts, which can bias comparison results. The authors illustrate these issues with toy examples and systematic simulations and offer suggestions for more rigorous comparison practices. To show the practical significance of these lessons, the authors replicate an empirical study that examines the temporal trend of modularity scores for job mobility networks to evaluate the evolution of mobility boundary rigidity in the U.S. labor market.
Protest surveys are one of the most common methods for studying participation in social movements. As the use of digital tools has become more common in academic research, they are being applied to facilitate data collection at protest events. The authors analyze the benefits and drawbacks of these digital tools for data collection at protest events. They present the results of a field experiment that tests two increasingly common forms of data collection: surveys collected through electronic tablets and through QR (quick response) codes. The field experiment was conducted at four anti-Trump rallies on the east coast of the United States in 2025. The authors find benefits and drawbacks to each technique. The findings focus on differences in response rates, delayed refusal, and the demographics of survey respondents. The authors conclude by discussing how to apply these findings to collect the most robust sample of protest participants.
Large language models (LLMs) increasingly serve as humanlike decision-making agents in social science and applied settings. These LLM agents are typically assigned humanlike characters and placed in real-life contexts. However, how these characters and contexts shape an LLM's behavior remains underexplored. In this study the author proposes and tests methods for probing, quantifying, and modifying an LLM's internal representations in a dictator game, a classic behavioral experiment on fairness and prosocial behavior. The author extracts "vectors of variable variations" (e.g., "male" to "female") from the LLM's internal state. Manipulating these vectors during the model's inference can substantially alter how those variables relate to the model's decision making. This approach offers a principled way to study and regulate how social concepts can be encoded and engineered within transformer-based models, with implications for alignment, debiasing, and designing artificial intelligence agents for social simulations in both academic and commercial applications, strengthening sociological theory and measurement.
