Sociological Methods & Research最新文献_第4页

The Unrealized Potential of Audits: Applicant-Side Inequalities in Effort, Opportunities, and Certainty 未实现的审计潜力：申请人在努力、机会和确定性方面的不平等

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-05-22 DOI: 10.1177/00491241251338240

Mike Vuolo, Sadé L. Lindsay, Vincent J. Roscigno, Shawn D. Bushway

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引用次数: 0

Updating “The Future of Coding”: Qualitative Coding with Generative Large Language Models 更新“编码的未来”：用生成式大型语言模型进行定性编码

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-05-21 DOI: 10.1177/00491241251339188

Nga Than, Leanne Fan, Tina Law, Laura K. Nelson, Leslie McCall

引用次数: 0

Balancing Large Language Model Alignment and Algorithmic Fidelity in Social Science Research 社会科学研究中大语言模型一致性与算法保真度的平衡

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-05-21 DOI: 10.1177/00491241251342008

Alex Lyman, Bryce Hepner, Lisa P. Argyle, Ethan C. Busby, Joshua R. Gubler, David Wingate

引用次数: 0

Generative AI Meets Open-Ended Survey Responses: Research Participant Use of AI and Homogenization 生成人工智能满足开放式调查回应：研究参与者使用人工智能和同质化

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-05-07 DOI: 10.1177/00491241251327130

Simone Zhang, Janet Xu, AJ Alvero

引用次数: 0

Integrating Generative Artificial Intelligence into Social Science Research: Measurement, Prompting, and Simulation 将生成式人工智能整合到社会科学研究：测量、提示和模拟

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-05-07 DOI: 10.1177/00491241251339184

Thomas Davidson, Daniel Karell

引用次数: 0

Large Language Models for Text Classification: From Zero-Shot Learning to Instruction-Tuning 用于文本分类的大型语言模型：从零学习到指令调整

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-04-24 DOI: 10.1177/00491241251325243

Youngjin Chae, Thomas Davidson

{"title":"Large Language Models for Text Classification: From Zero-Shot Learning to Instruction-Tuning","authors":"Youngjin Chae, Thomas Davidson","doi":"10.1177/00491241251325243","DOIUrl":"https://doi.org/10.1177/00491241251325243","url":null,"abstract":"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.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"72 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143866960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Conceptualizing Job and Employment Concepts for Earnings Inequality Estimands With Linked Employer-Employee Data 1 概念化工作和就业概念与关联雇主-雇员数据的收入不平等估计1

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-04-24 DOI: 10.1177/00491241251334124

Donald Tomaskovic-Devey, Chen-Shuo Hong

引用次数: 0

The Target Study: A Conceptual Model and Framework for Measuring Disparity 目标研究：衡量差异的概念模型和框架

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-04-22 DOI: 10.1177/00491241251314037

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

引用次数: 0

Networks Beyond Categories: A Computational Approach to Examining Gender Homophily 超越类别的网络：研究性别同源性的计算方法

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-04-22 DOI: 10.1177/00491241251321152

Chen-Shuo Hong

{"title":"Networks Beyond Categories: A Computational Approach to Examining Gender Homophily","authors":"Chen-Shuo Hong","doi":"10.1177/00491241251321152","DOIUrl":"https://doi.org/10.1177/00491241251321152","url":null,"abstract":"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.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"32 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143862884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Mixed Subjects Design: Treating Large Language Models as Potentially Informative Observations 混合主题设计：将大型语言模型视为潜在的信息观察

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-04-22 DOI: 10.1177/00491241251326865

David Broska, Michael Howes, Austin van Loon

{"title":"The Mixed Subjects Design: Treating Large Language Models as Potentially Informative Observations","authors":"David Broska, Michael Howes, Austin van Loon","doi":"10.1177/00491241251326865","DOIUrl":"https://doi.org/10.1177/00491241251326865","url":null,"abstract":"Large language models (LLMs) provide cost-effective but possibly inaccurate predictions of human behavior. Despite growing evidence that predicted and observed behavior are often not interchangeable , there is limited guidance on using LLMs to obtain valid estimates of causal effects and other parameters. We argue that LLM predictions should be treated as potentially informative observations, while human subjects serve as a gold standard in a mixed subjects design . This paradigm preserves validity and offers more precise estimates at a lower cost than experiments relying exclusively on human subjects. We demonstrate—and extend—prediction-powered inference (PPI), a method that combines predictions and observations. We define the PPI correlation as a measure of interchangeability and derive the effective sample size for PPI. We also introduce a power analysis to optimally choose between informative but costly human subjects and less informative but cheap predictions of human behavior. Mixed subjects designs could enhance scientific productivity and reduce inequality in access to costly evidence.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"4 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143862886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0