Research on social problems often analyzes how different groups think or act in relation to a single issue. Less frequent are studies of how a single group thinks or acts in relation to many phenomena, any of which may be construed as problematic to a greater or lesser degree. We take this multiple phenomena - single social position approach and analyze why the Toronto and Region Conservation Authority (TRCA), a supra-municipal government agency, discusses some non-native species as more "invasive" than others. We use word embeddings to measure variation in the strength of association between different species and invasiveness in 599 of the TRCA's policy documents and employ generalized additive models to explain this variation. We find that the "invasive" meaning is more strongly associated with species that are easier to observe, access, control, or manage in the TRCA's urban context, which we term workability. Species that are terrestrial, sessile, and moderately abundant are more strongly associated with invasiveness than mobile, aquatic, and hyperabundant species. These findings suggest that problem managers conceive of issues they are responsible for managing according to how actionable problems appear. We propose workability as a key analytic lens for understanding how problem managers make decisions and construct meaning. We situate this contribution in the context of four research designs for studying social problems that we term comparative problem-solving designs.

Analyzing social change requires detecting patterns of continuity and difference over time. While time-series clustering offers a valuable approach, existing techniques are often limited by assuming fixed cluster definitions and static assignments of entities to clusters. To address these limitations, we introduce a unified framework of temporal clustering methods that allows for both dynamic cluster definitions and the transition of entities between clusters, generalizing and extending previous work. We also provide new algorithms for this dynamic clustering that optimize global objectives, with optional constraints on the transitions of entities across clusters. This framework expands the methodological toolkit for analyzing social change, and we provide guidelines for its application. We illustrate our approach with three case studies: polarization of social and political attitudes across U.S. states; cross-national cultural change; and the evolution of neighborhood business patterns. We conclude with directions for further research.

Large Language Models (LLMs) are increasingly used as proxies for human perception in urban analysis, yet it remains unclear whether persona prompting produces meaningful and reproducible behavioral diversity. We investigate whether distinct personas influence urban sentiment judgments generated by multimodal LLMs. Using a factorial set of personas spanning gender, economic status, political orientation, and personality, we instantiate multiple agents per persona to evaluate urban scene images from the PerceptSent dataset and assess both within-persona consistency and cross-persona variation. Results show strong convergence among agents sharing a persona, indicating stable and reproducible behavior. However, cross-persona differentiation is limited: economic status and personality induce statistically detectable but practically modest variation, while gender shows no measurable effect and political orientation only negligible impact. Agents also exhibit an extremity bias, collapsing intermediate sentiment categories common in human annotations. As a result, performance remains strong on coarse-grained polarity tasks but degrades as sentiment resolution increases, suggesting that simple label-based persona prompting does not capture fine-grained perceptual judgments. To isolate the contribution of persona conditioning, we additionally evaluate the same model without personas. Surprisingly, the no-persona model sometimes matches or exceeds persona-conditioned agreement with human labels across all task variants, suggesting that simple label-based persona prompting may add limited annotation value in this setting.

As AI systems are deployed across increasingly diverse social contexts, alignment can no longer be framed as the optimization of a single, unified set of values. Instead, systems must be able to recognize, represent, and respond to multiple legitimate perspectives. This has led to growing interest in pluralistic alignment, which seeks to move beyond one-size-fits-all models of appropriate behaviour. However, current approaches often lack a clear account of how values are socially organized, contested, and coordinated in practice. In this paper, we argue that social theory provides essential conceptual and design resources for addressing these challenges. Drawing on established traditions in sociology, we show how perspectives can be understood as structured by roles, shaped through interaction, and distributed across fields of power and expertise. We translate these insights into concrete implications for AI system design, including role-based representations, structured coordination among perspectives, and context-sensitive evaluation. For agentic systems, this requires aligning not only final outputs, but also the role activations, deliberative traces, aggregation rules, and feedback loops through which those outputs are produced. Our contribution is to reposition pluralistic alignment as a problem of socially grounded coordination rather than output diversification. We outline a design space for systems that engage multiple perspectives in structured and accountable ways, and we identify directions for future work to implement and empirically evaluate these approaches in real-world settings.

This study examines how persona prompting shapes language generated by two multimodal large language models in urban perception, a setting for examining subjective interpretations of shared visual evidence. We organize outputs into three functional levels: descriptive grounding (captions), intermediate semantic layer (perception tags), and interpretive framing (justifications). Using approximately 60,000 persona-conditioned annotations per model from Qwen3-VL-8B and Gemma-4-E4B-it, we find that captions converge strongly across persona profiles and show only small attribute-associated differences. Justifications vary substantially more: economic status produces the largest difference in both models, with political orientation and personality also prominent. Paired image-level comparisons confirm larger justification than caption differences for these three attributes. For perception tags, personas sharing the same attribute level produce more similar tag sets than personas with different attribute levels, with the largest separation observed for economic status. Exploratory topic analysis further reveals persona-specific evaluative emphasis. Across models, profile-pair similarity patterns are strongly correlated for all three output types, although agreement is lowest for justifications. Overall, persona prompting affects interpretive framing more strongly than descriptive grounding.

Since the 1960s, "grand theory" has increasingly been used to categorize and discuss sociological theories. The result of this is, ironically, a discourse in which it is neither clear which theories count as "grand" nor what such a characterization indicates. By rooting our analysis in a corpus of 3,673 chapters of English-language textbooks, we offer an empirically-grounded contribution to the debate on the past and future of "grand theory". First, we demonstrate how the composition and scope of "grand theory" have changed between 1967 and 2018. Second, we reconstruct the shifting semantic associations around the concept. Third, we qualitatively distinguish four strategic usages of "grand theory" within processes of canonization, which either intend to preserve, reduce, limit, or expand the composition of the sociological theory canon. In sum, we argue that the diverse rhetorical mobilization of the concept has institutionalized it as one of the central discursive structuring devices in debates about the sociological theory canon, effectively reifying the idea that the term designates a substantive form of theory while simultaneously obscuring "grand theory's" contested content.

We present CityHood, an interactive and explainable recommendation system that suggests cities and neighborhoods based on users' areas of interest. The system models user interests leveraging large-scale Google Places reviews enriched with geographic, socio-demographic, political, and cultural indicators. It provides personalized recommendations at city (Core-Based Statistical Areas - CBSAs) and neighborhood (ZIP code) levels, supported by an explainable technique (LIME) and natural-language explanations. Users can explore recommendations based on their stated preferences and inspect the reasoning behind each suggestion through a visual interface. The demo illustrates how spatial similarity, cultural alignment, and interest understanding can be used to make travel recommendations transparent and engaging. This work bridges gaps in location-based recommendation by combining a kind of interest modeling, multi-scale analysis, and explainability in a user-facing system.

Why are some neighborhoods strongly connected while others remain isolated? Although standard explanations focus on demographics, economics, and geography, movement across the city may also depend on cultural styles and amenity mix. This study proposes a relational, cross-national model in which local culture and amenity mix alignment creates a "soft infrastructure" of urban mobility, i.e., symbolic cues and functional features that shape expectations about the character of places. Using 650 million Google Places reviews to measure co-visitation between U.S. ZIP codes and 30 million Canadian change-of-address to track residential mobility, results show that neighborhoods with similar cultural styles and amenities are significantly more connected. These effects persist even after controlling for race, income, education, politics, housing costs, and distance. Urban cohesion and segregation depend not only on who lives where or how far apart neighborhoods are, but on the shared cultural and material ecologies that structure movement across the city.

Understanding why individuals choose to visit particular cities and specific neighborhoods within them is essential for advancing both urban mobility research and personalized tourism technologies. This paper proposes a novel multi-level (city and neighborhood levels), explainable recommendation framework that models user interest based on area similarities across geographic, demographic, cultural, and venue-category dimensions. Our approach predicts user interest through a behaviorally informed, interpretable machine learning model. Using large-scale review data from Google Places, enriched with U.S. Census, political, and cultural indicators, we analyze mobility through the lens of high-interest and lowinterest divisions and two behavioral archetypes: returners (who repeatedly visit familiar areas) and explorers (who seek out new destinations). Results show that explorers are more interested in geographically clustered cities, suggesting a search for new experiences in nearby locations. In contrast, returners attach to areas that align with their past experiences (e.g., venue categories). Beyond good predictive performance, our system provides natural-language explanations for each recommendation, offering actionable insights into user behavior. A demonstration system illustrates how our approach enables transparent, behavior-informed travel recommendations. This work bridges gaps in urban AI by integrating spatial granularity, behavioral segmentation, and explainability.

This study investigates methods using a global data source, Google Places, to identify culturally similar urban areas without relying on difficult-to-access data like user preferences shown through checkins. We propose and assess a simple method requiring only information about place types and their frequency in the studied areas, and a more advanced method that enhances venue categories using Scenes Theory it helps us understand the cultural significance of everyday urban life. We tested our methods in 14 cities worldwide and all US states. The results suggest that a straightforward approach based on category frequencies can highlight major cultural differences. However, the Scenes Theory-based method provides a better understanding of cultural nuances, as the ones supported by survey data.

In the global arena of municipal policymaking, cities do not merely address local concerns but actively engage with other cities in a global relational space, referencing and being referenced by others. Within these networks, certain “model cities” emerge, linking urban transformation strategies to specific city experiences. Although much research focuses on the production of model cities—how they gain prominence and status—less attention has been given to the peer cities that reference them. The authors examine how model cities rise by analyzing “referential styles”: the ways cities express interest in one another. Drawing on urban sociology, cultural theory, and network analysis, the authors propose two propositions to explain the forces that influence referencing styles: the cultural domination proposition, which suggests that the characteristics of referenced cities shape how they are discussed, and the networks from culture proposition, which suggests that referencing cities’ attributes drive their interpretations. Using public art policy documents (1959–2020) from 26 major anglophone cities and computational techniques, the authors investigate the referential styles cities use to discuss one another. The authors find support for both propositions: although dominant cities determine where to look, it is often the attributes of referencing cities that determine how to look that shape the referencing style. These results suggest orienting policy-mobility research more toward the peer-network ecologies that actively construct urban meaning.

Daniel Silver on The Set Up, The Tour Guide, Music/City, and The City and the Hospital.

Temporal clustering extends the conventional task of data clustering by grouping time series data according to shared temporal trends across sociospatial units, with diverse applications in the social sciences, especially urban science. The two dominant methods are as follows: Time Series Clustering (TSC), with dynamic cluster centres but static labels for each entity, and Sequence Label Analysis (SLA), with static cluster centres but dynamic labels. To implement the universe of models spanning the design space between TSC and SLA, we present tscluster, an open-source Python framework. tscluster offers: (1) several innovative techniques, such as Bounded Dynamic Clustering (BDC), that are not available in existing libraries, allowing users to set an upper bound on the number of label changes and identify the most dynamically evolving time series; (2) a user-friendly interface for applying and comparing these methods; (3) globally optimal solutions for the clustering objective by employing a mixed-integer linear programming formulation, enhancing the reproducibility and robustness of the results in contrast to existing methods based on initialization-sensitive local optimization; and (4) a suite of visualization tools for interpretability and comparison of clustering results. We present our framework using a case study of neighbourhood change in Toronto, comparing two methods available in tscluster. Supplemental materials provide an additional case study of local business development in Chicago and a detailed mathematical exposition of our framework. tscluster can be installed via PyPI (pypi.org/project/tscluster), and the source code is accessible on Github (github.com/tscluster-project/tscluster). Documentation is available online at the tscluster website (tscluster.readthedocs.io).

The urban policy mobility literature describes the widespread circulation of policy ideas while highlighting their mutations along the way. At the same time, the literature often analyzes the localization of such ideas by examining their adoption in one or several cities. To better understand policy replications and mutations, we develop theoretical and methodological strategies that provide sensitivity to both local distinctiveness and global variability. We build on the Urban Policy Mobility literature and combine it with ecological theories of conceptual spaces to develop the concept of Urban Model Spaces—a matrix of discursive possibilities evolving from the accumulated replications and localizations of a model. We articulate it via three core properties central to Urban Policy Mobility—Temporality, Scale, and Position—and test how they shape the emergence of policy discourses. To demonstrate the concept we analyze public art policy and the funding mechanism of the Percent for Art ordinance from 26 cities combining Structural Topic Modeling and regression analysis.

O conhecimento a respeito das características dos diferentes grupos culturais que existem no mundo e a identificação de similaridades culturais entre suas respectivas áreas de ocupação podem trazer diversos benefícios econômicos e sociais, como a recomendação de locais sob critérios culturais. Pesquisas referentes ao estudo dessas diferentes culturas são realizadas, em grande parte, de maneira tradicional, as quais são caras e não escalam. Dessa forma, este trabalho consiste em obter características relevantes de áreas urbanas utilizando dados geolocalizados de fontes da web, e aplicar uma metodologia que enriquece esses dados obtidos para a geração de uma assinatura cultural de áreas urbanas. Em uma aplicação prática da proposta, o resultado se mostra muito coerente, separando os bairros de Curitiba em clusters com características culturais distintas.

Modern data-oriented applications often require integrating data from multiple heterogeneous sources. When these datasets share attributes, but are otherwise unlinked, there is no way to join them and reason at the individual level explicitly. However, as we show in this work, this does not prevent probabilistic reasoning over these heterogeneous datasets even when the data and shared attributes exhibit significant mismatches that are common in real-world data. Different datasets have different sample biases, disagree on category definitions and spatial representations, collect data at different temporal intervals, and mix aggregate-level with individual data. In this work, we demonstrate how a set of Bayesian network motifs allows all of these mismatches to be resolved in a composable framework that permits joint probabilistic reasoning over all datasets without manipulating, modifying, or imputing the original data, thus avoiding potentially harmful assumptions. We provide an open source Python tool that encapsulates our methodology and demonstrate this tool on a number of real-world use cases.

When sociologists examine the content of sociological knowledge, they typically engage in textual analysis. Conversely, this paper examines the relationship between theory figures and causal claims. Analyzing a random sample of articles from prominent sociology journals, we find several notable trends in how sociologists both describe and visualize causal relationships, as well as how these modes of representation interrelate. First, we find that the modal use of arrows in sociology are as expressions of causal relationship. Second, arrow-based figures are connected to both strong and weak causal claims, but that strong causal claims are disproportionately found in U.S. journals compared to European journals. Third, both causal figures and causal claims are usually central to the overarching goals of articles. Lastly, the strength of causal figures typically fits with the strength of the textual causal claims, suggesting that visualization promotes clearer thinking and writing about causal relationships. Overall, our findings suggest that arrow-based figures are a crucial cognitive and communicative resource in the expression of causal claims.