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社會(huì)學(xué)國(guó)際頂刊

Sociological Methods & Research

（《社會(huì)學(xué)方法與研究》）

最新目錄及摘要

期刊簡(jiǎn)介

Sociological Methods & Research

About SMR

Sociological Methods & Research is a quarterly journal devoted to sociology as a cumulative empirical science. The objectives of SMR are multiple, but emphasis is placed on articles that advance the understanding of the field through systematic presentations that clarify methodological problems and assist in ordering the known facts in an area. Review articles will be published, particularly those that emphasize a critical analysis of the status of the arts, but original presentations that are broadly based and provide new research will also be published. Intrinsically, SMR is viewed as substantive journal but one that is highly focused on the assessment of the scientific status of sociology. The scope is broad and flexible, and authors are invited to correspond with the editors about the appropriateness of their articles.

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Current Issue

SMR 為季刊，最新一期內(nèi)容（Volume 55 Number 2, May 2026）共計(jì)10篇文章，詳情如下。

Contents

原文摘要

Sociological Methods & Research

When to Use Counterfactuals in Causal Historiography: Methods for Semantics and Inference

Tay Jeong

According to the interventionist framework of actual causality, causal claims in history are ultimately claims about special types of functional dependencies between variables, which consist not only of actual events but also of corresponding counterfactual states of affairs. Instead of advocating the methodological use of counterfactuals tout court, we propose specific circumstances in historical writing where counterfactual reasoning comes in most handy. At the level of semantics, that is, the specification of the variables and their possible values, an explicit specification of the latent contrast classes becomes particularly useful in situations where one may be prompted to take an event that is pre-empted by the antecedent of interest as its proper causal contrast. At the level of inference, we argue that cases in which two or more antecedents appear to be playing a similar role tend to fumble our pretheoretical intuition about cause and propose a sequence of counterfactual tests based on actual examples from causal historiography.

The Target Study: A Conceptual Model and Framework for Measuring Disparity

John W. Jackson, Yea-Jen Hsu, Raquel C. Greer, Romsai T. Boonyasai, Chanelle J. Howe

We present a conceptual model to measure disparity—the target study—where social groups may be similarly situated (i.e., balanced) on allowable covariates. Our model, based on a sampling design, does not intervene to assign social group membership or alter allowable covariates. To address nonrandom sample selection, we extend our model to generalize or transport disparity or to assess disparity after an intervention on eligibility-related variables that eliminates forms of collider-stratification. To avoid bias from differential timing of enrollment, we aggregate time-specific study results by balancing calendar time of enrollment across social groups. To provide a framework for emulating our model, we discuss study designs, data structures, and G-computation and weighting estimators. We compare our sampling-based model to prominent decomposition-based models used in healthcare and algorithmic fairness. We provide R code for all estimators and apply our methods to measure health system disparities in hypertension control using electronic medical records.

Networks Beyond Categories: A Computational Approach to Examining Gender Homophily

Chen-Shuo Hong

Social networks literature has explored homophily, the tendency to associate with similar others, as a critical boundary-making process contributing to segregated networks along the lines of identities. Yet, social network research generally conceptualizes identities as sociodemographic categories and seldom considers the inherently continuous and heterogeneous nature of differences. Drawing upon the infracategorical model of inequality, this study demonstrates that a computational approach – combining machine learning and exponential random graph models (ERGMs) – can capture the role of categorical conformity in network structures. Through a case study of gender segregation in friendships, this study presents a workflow for developing a machine-learning-based gender conformity measure and applying it to guide the social network analysis of cultural matching. Results show that adolescents with similar gender conformity are more likely to form friendships, net of homophily based on categorical gender and other controls, and homophily by gender conformity mediates homophily by categorical gender. The study concludes by discussing the limitations of this computational approach and its unique strengths in enhancing theories on categories, boundaries, and stratification.

Large Language Models for Text Classification: From Zero-Shot Learning to Instruction-Tuning

Youngjin Chae, Thomas Davidson

Large language models (LLMs) have tremendous potential for social science research as they are trained on vast amounts of text and can generalize to many tasks. We explore the use of LLMs for supervised text classification, specifically the application to stance detection, which involves detecting attitudes and opinions in texts. We examine the performance of these models across different architectures, training regimes, and task specifications. We compare 10 models ranging in size from tens of millions to hundreds of billions of parameters and test four distinct training regimes: Prompt-based zero-shot learning and few-shot learning, fine-tuning, and instruction-tuning, which combines prompting and fine-tuning. The largest, most powerful models generally offer the best predictive performance even with little or no training examples, but fine-tuning smaller models is a competitive solution due to their relatively high accuracy and low cost. Instruction-tuning the latest generative LLMs expands the scope of text classification, enabling applications to more complex tasks than previously feasible. We offer practical recommendations on the use of LLMs for text classification in sociological research and discuss their limitations and challenges. Ultimately, LLMs can make text classification and other text analysis methods more accurate, accessible, and adaptable, opening new possibilities for computational social science.

The Insight-Inference Loop: Efficient Text Classification via Natural Language Inference and Threshold-Tuning

Sandrine Chausson, Marion Fourcade, David J. Harding, Bj?rn Ross, Grégory Renard

Modern computational text classification methods have brought social scientists tantalizingly close to the goal of unlocking vast insights buried in text data—from centuries of historical documents to streams of social media posts. Yet three barriers still stand in the way: the tedious labor of manual text annotation, the technical complexity that keeps these tools out of reach for many researchers, and, perhaps most critically, the challenge of bridging the gap between sophisticated algorithms and the deep theoretical understanding social scientists have already developed about human interactions, social structures, and institutions. To counter these limitations, we propose an approach to large-scale text analysis that requires substantially less human-labeled data, and no machine learning expertise, and efficiently integrates the social scientist into critical steps in the workflow. This approach, which allows the detection of statements in text, relies on large language models pre-trained for natural language inference, and a “few-shot” threshold-tuning algorithm rooted in active learning principles. We describe and showcase our approach by analyzing tweets collected during the 2020 U.S. presidential election campaign, and benchmark it against various computational approaches across three datasets.

Examining Variation in Survey Costs Across Surveys

Kristen Olson, John Stevenson, Nadia Assad, Lindsey Witt-Swanson, Cameron P.E. Jones, Amanda Ganshert, Jennifer Dykema

Self-administered surveys may be administered with a single mode or mixed data collection modes. How mixing modes of data collection affects survey costs is not well understood. We examine whether cost structures differ for mail-only versus web+mail mixed-mode surveys, what design features are associated with costs, and whether survey costs are associated with response rates. Using administrative survey cost data from two academic survey centers, we find that survey costs per sampled unit and per complete vary substantially across individual surveys. The average cost per sampled unit is surprisingly similar across mail-only and web+mail surveys. How the budget is allocated across printing, postage, incentive, and staff time varies across these designs: printing and postage costs are higher in mail-only surveys, and more of the budget is allocated to incentive costs and project management costs in web+mail surveys. Furthermore, higher cost surveys are associated with higher response rates, particularly for incentive costs.

The Rise in Occupational Coding Mismatches and Occupational Mobility, 1991–2020

Andrew Taeho Kim, ChangHwan Kim

Occupation is a construct prone to classification mismatches by coders and description inconsistency by respondents. We explore whether mismatches in occupational coding have recently increased, what factors are associated with the rise in mismatches, and how the rise affects estimates of intragenerational occupational mobility. Utilizing the 1991–2020 Annual Social and Economic Supplement of the Current Population Survey, which collects information on respondents’ current occupation and the previous year’s main occupation, we identify coding mismatches and compare the probabilities of occupational mobility based on four combinations of two variables. Our results show that not only do the estimates of occupational mobility between two adjacent years vary substantially across measures, but also that the magnitudes of intragenerational occupational mobility across measures become increasingly decoupled over time. We demonstrate that the likely cause of this divergence is the rise in coding mismatches between coders. We discuss the implications of our findings.

Using Google Maps to Generate Organizational Sampling Frames

Brad R Fulton, David P King

Organizational researchers use a variety of methods to obtain sampling frames. The utility of these methods, however, is constrained by access restrictions, limited coverage, prohibitive costs, and cumbersome formats. This article presents a new method for generating organizational sampling frames that is cost-effective, uses publicly available data, and can produce sampling frames for many geographic areas in the U.S. The Python-based program we developed systematically scans the Google Maps platform to identify organizations of interest and retrieve their contact information. We demonstrate the program's viability and utility by generating a sampling frame of religious congregations in the U.S. To assess Google Maps’ coverage and representativeness of such congregations, we examined two nationally representative samples of congregations and censuses of congregations in a small, medium, and large city. We found that Google Maps contains approximately 98% of those congregations––extensive coverage that ensures a high degree of representativeness. This study provides evidence that using Google Maps to generate sampling frames can improve the process for obtaining representative samples for organizational studies by reducing costs, increasing efficiency, and providing greater coverage and representativeness.

Improving Cross-Cultural Comparability of Measures on Gender and Age Stereotypes by Means of Piloting Methods

Natalja Menold, Patricia Hadler, Cornelia Neuert

The study addresses the effects of piloting methods on the cross-cultural comparability and reliability of the measurement of gender and age stereotypes. We conducted a summative evaluation of expert reviews, cognitive pretests and web probing. We first piloted a gender role, an ageism, and a children stereotypes instrument in German and American English. We then randomly assigned the original and piloted versions to respondents in Germany and the United States using an online survey experiment and quota samples. No configural invariance was shown by the original instruments and the reliability of the gender role instrument was insufficiently low. The results show that piloting methods increased reliability and improved measurement invariance, although the effects varied by topic. Cross-cultural expert reviews and web probing provided more consistent results than other methods. A combination of web probing and cross-cultural expert reviews can maximize both reliability and measurement invariance.

Locating Cultural Holes Brokers in Diffusion Dynamics Across Bright Symbolic Boundaries

Diego F. Leal

Although the literature on cultural holes has expanded considerably in recent years, there is no concrete measure in that literature to locate cultural holes brokers. This article develops a conceptual framework grounded in social network theory and cultural sociology to propose a specific solution to fill this measurement gap. Agent-based computational experiments are leveraged to develop a theoretical test of the analytic purchase and distinctiveness of the proposed measure, termed potential for intercultural brokerage (PIB). Results demonstrate the effectiveness of PIB in locating early adopters that can achieve widespread levels of diffusion in societies segregated along bright symbolic boundaries. Findings also show the superiority of PIB when compared to classic alternative measures in the network literature that focus on locating early adopters based on structural holes (e.g., network constraint, effective size), geodesics (e.g., betweenness centrality), and degree (e.g., degree centrality), among other classic network measures. Broader implications of these findings for brokerage theory are discussed herein.

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《中國(guó)社會(huì)學(xué)學(xué)刊》（The Journal of Chinese Sociology）于2014年10月由中國(guó)社會(huì)科學(xué)院社會(huì)學(xué)研究所創(chuàng)辦。作為中國(guó)大陸第一本英文社會(huì)學(xué)學(xué)術(shù)期刊，JCS致力于為中國(guó)社會(huì)學(xué)者與國(guó)外同行的學(xué)術(shù)交流和合作打造國(guó)際一流的學(xué)術(shù)平臺(tái)。JCS由全球最大科技期刊出版集團(tuán)施普林格·自然(Springer Nature)出版發(fā)行，由國(guó)內(nèi)外頂尖社會(huì)學(xué)家組成強(qiáng)大編委會(huì)隊(duì)伍，采用雙向匿名評(píng)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(fā)布的2023年度《期刊引證報(bào)告》（JCR）中首次獲得影響因子并達(dá)到1.5（Q3）。2025年JCS最新影響因子1.3，位列社會(huì)學(xué)領(lǐng)域期刊全球前53%（Q3）。

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