外刊吃瓜 |《社會學方法論》最新目錄與摘要

外刊吃瓜

— The Journal of Chinese Sociology —

本周JCS外刊吃瓜

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社會學國際頂刊

Sociological Methodology

（《社會學方法論》）

最新目錄及摘要

期刊簡介

Sociological Methodology

About SM

Sociological Methodology (SM) is the only American Sociological Association periodical publication devoted entirely to research methods. It is a compendium of new and sometimes controversial advances in social science methodology. Contributions come from diverse areas and have something new and useful--and sometimes surprising--to say about a wide range of methodological topics.

SM seeks contributions to qualitative, quantitative, and mixed methods that address the full range of methodological problems confronted by empirical research in the social sciences, including conceptualization, data analysis, data collection, measurement, modeling, and research design. Such contributions must have relevance to sociological research and practice. The journal provides a forum for engaging the philosophical issues that underpin sociological research.

Papers published in SM are original methodological contributions, including new methodological developments, applications of recent developments that provide new sociological insights, and critical evaluative discussions of research practices and traditions. SM encourages the inclusion of applications to real-world sociological data.

Journal Metrics

Current Issue

Sociological Methodology 每年發布兩期，最新一期的內容（Volume 56 Issue 1, February 2026）共計5篇文章，詳情如下。

Contents

原文摘要

Sociological Methodology

Joint Text-and-Image Clustering for Social Science Research

Han Zhang, Ryan Leung

Automated text analysis is becoming extremely popular and image analysis is gaining interest. However, multimodal analysis that combines both text and image information remains rare, even though many real-world data are intrinsically multimodal, such as social media posts. The authors compare three practical workflows for clustering text–image pairs: (1) label-level combination, which clusters text and image separately and combines the resulting labels; (2) vector-level combination, which clusters concatenated embeddings extracted from each modality; and (3) joint embedding, which clusters unified representations from multimodal embedding models such as Contrastive Language-Image Pre-training. The authors also introduce a set of reusable evaluation tools to help researchers compare, validate, and benchmark multimodal clustering workflows: adjusted mutual information to assess text–image alignment, the S_DbW index to evaluate number of clusters, and within-cluster consistency to validate interpretability. The authors validate the methods on a Chinese protest data set from social media with 336,921 text–image pairs and test robustness and scope conditions using a smaller U.S. news data set on gun violence with 1,297 news headlines. The authors find that when text and image provide distinct, nonoverlapping information, the second and third methods outperform the first. This study serves as a bridge between the text-as-data and image-as-data communities.

Computational Basis of Large Language Models’ Decision Making in Social Simulation

Ji Ma

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.

Counterfactual Road Networks: A Method for Examining Social and Spatial Division in Road Networks

Elizabeth Roberto, Yu Zhu, Santiago Segarra, Jaleh Jalili

Racism, discriminatory practices, institutional bias, and systematic exclusion can take lasting physical form in the built environment. There has been growing attention to the long-term consequences of housing policies and practices on social and economic outcomes, including residential segregation, but comparatively less attention to other aspects of the built environment, such as road networks and spatial (dis)connectivity. In this article, the authors introduce a novel method that constructs counterfactual road networks by identifying missing road segments that would be expected to exist in a city’s road network, given the surrounding infrastructure. The authors demonstrate the empirical application of the method by analyzing differences in racial composition and residential segregation for the observed and counterfactual road networks of five U.S. cities. The authors find that unexpected disconnectivity in a city’s road network is associated with greater differences in the racial composition of nearby areas and higher levels of segregation at the local and city levels. The present findings suggest that road networks warrant more attention as a factor that may contribute to the persistence of segregation.

Equivalence and Clustering in Worker Flows: Stochastic Blockmodels for the Analysis of Mobility Tables

Barum Park

Mobility scholars are increasingly turning to computational methods to analyze mobility tables. Most of these approaches start with the detection of mobility clusters, namely, sets of occupations within which the flow of workers is dense and across which the flow is sparse. Yet clustering is not the only way worker flows can be structured. This article shows how a degree-corrected stochastic blockmodel can detect patterns of mobility that are more general than clustering and consistent with the homogeneity criterion laid out by Goodman as well as the internal homogeneity thesis proposed by Breiger. Because of the intractable marginal likelihood of the model, parameters are estimated using a variational expectation maximization algorithm. Simulation results suggest the estimation algorithm successfully recovers (conditionally) stochastically equivalent mobility classes. Analysis of two real-world examples shows the model is able to detect meaningful mobility patterns, even in situations in which commonly used community detection algorithms fail.

Comparing Ecological Momentary Assessments and Time Diary Methods for Measuring Daily Life

Siyun Peng, Brea L. Perry, Adam R. Roth

A growing number of social scientists are using ecological momentary assessment (EMA) to observe how social forces operate in real time. However, the validity of EMA for measuring features of daily life—what people are doing, where, and with whom—remains uncertain. A key challenge is the lack of consensus across studies about how validity in EMA methods is defined and assessed. The authors address that gap by comparing EMA data (n?=?1,174) with time diary data (n?=?1,113) using two population-based samples. An advantage of large samples is the ability to evaluate the magnitude of bias rather than relying solely on p-values, as is common in small-sample studies. The authors find that both methods yield similar estimates of moments captured at home and in the workplace, supporting their validity in those contexts. However, EMA tends to overestimate moments spent alone compared with time diaries, likely because of moment selection bias. Moreover, large discrepancies in estimates for eating and drinking and household chores suggest that relying on primary activity reports can introduce significant bias for multitasked activities. Comparing these methods provides insight into their relative strengths and limitations, helping researchers assess the validity, potential biases, and interpretive implications of each across key domains of daily life.

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《中國社會學學刊》（The Journal of Chinese Sociology）于2014年10月由中國社會科學院社會學研究所創辦。作為中國大陸第一本英文社會學學術期刊，JCS致力于為中國社會學者與國外同行的學術交流和合作打造國際一流的學術平臺。JCS由全球最大科技期刊出版集團施普林格·自然(Springer Nature)出版發行，由國內外頂尖社會學家組成強大編委會隊伍，采用雙向匿名評審方式和“開放獲取”(open access)出版模式。JCS已于2021年5月被ESCI收錄。2022年，JCS的CiteScore分值為2.0（Q2），在社科類別的262種期刊中排名第94位，位列同類期刊前36%。2023年，JCS在科睿唯安發布的2023年度《期刊引證報告》（JCR）中首次獲得影響因子并達到1.5（Q3）。2025年JCS最新影響因子1.3，位列社會學領域期刊全球前53%（Q3）。

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