Sociological Methods & Research最新文献_第5页

Social Mobility as Causal Intervention 社会流动作为因果干预

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-04-21 DOI: 10.1177/00491241251320963

Lai Wei, Yu Xie

{"title":"Social Mobility as Causal Intervention","authors":"Lai Wei, Yu Xie","doi":"10.1177/00491241251320963","DOIUrl":"https://doi.org/10.1177/00491241251320963","url":null,"abstract":"The study of mobility effects is an important subject of study in sociology. Empirical investigations of individual mobility effects, however, have been hindered by one fundamental limitation, the unidentifiability of mobility effects when origin and destination are held constant. Given this fundamental limitation, we propose to reconceptualize mobility effects from the micro- to macro-level. Instead of micro-level mobility effects, the primary focus of the past literature, we ask alternative research questions about macro-level mobility effects: What happens to the population distribution of an outcome if we manipulate the mobility regime, that is, if we alter the observed association between social origin and social destination? We relate individual-level mobility experience to macro-level mobility effects under special interventions. The proposed method bridges the macro and micro agendas in social stratification research, and has wider applications in social stratification beyond the study of mobility effects. We illustrate the method with two analyses that evaluate the impact of social mobility on average fertility and income inequality in the United States. We provide an open-source software, the R package socmob , that implements the method.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"1 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857717","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

Correcting the Measurement Errors of AI-Assisted Labeling in Image Analysis Using Design-Based Supervised Learning 基于设计的监督学习修正图像分析中人工智能辅助标注的测量误差

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-04-21 DOI: 10.1177/00491241251333372

Alessandra Rister Portinari Maranca, Jihoon Chung, Musashi Hinck, Adam D. Wolsky, Naoki Egami, Brandon M. Stewart

{"title":"Correcting the Measurement Errors of AI-Assisted Labeling in Image Analysis Using Design-Based Supervised Learning","authors":"Alessandra Rister Portinari Maranca, Jihoon Chung, Musashi Hinck, Adam D. Wolsky, Naoki Egami, Brandon M. Stewart","doi":"10.1177/00491241251333372","DOIUrl":"https://doi.org/10.1177/00491241251333372","url":null,"abstract":"Generative artificial intelligence (AI) has shown incredible leaps in performance across data of a variety of modalities including texts, images, audio, and videos. This affords social scientists the ability to annotate variables of interest from unstructured media. While rapidly improving, these methods are far from perfect and, as we show, even ignoring the small amounts of error in high accuracy systems can lead to substantial bias and invalid confidence intervals in downstream analysis. We review how using design-based supervised learning (DSL) guarantees asymptotic unbiasedness and proper confidence interval coverage by making use of a small number of expert annotations. While originally developed for use with large language models in text, we present a series of applications in the context of image analysis, including an investigation of visual predictors of the perceived level of violence in protest images, an analysis of the images shared in the Black Lives Matter movement on Twitter, and a study of U.S. outlets reporting of immigrant caravans. These applications are representative of the type of analysis performed in the visual social science landscape today, and our analyses will exemplify how DSL helps us attain statistical guarantees while using automated methods to reduce human labor.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"3 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857722","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

Machine Bias. How Do Generative Language Models Answer Opinion Polls? 机器的偏见。生成语言模型如何回答民意调查？

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-04-21 DOI: 10.1177/00491241251330582

Julien Boelaert, Samuel Coavoux, Étienne Ollion, Ivaylo Petev, Patrick Präg

引用次数: 0

The Insight-Inference Loop: Efficient Text Classification via Natural Language Inference and Threshold-Tuning 洞察-推理循环：基于自然语言推理和阈值调优的高效文本分类

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-04-19 DOI: 10.1177/00491241251326819

Sandrine Chausson, Marion Fourcade, David J. Harding, Björn Ross, Grégory Renard

{"title":"The Insight-Inference Loop: Efficient Text Classification via Natural Language Inference and Threshold-Tuning","authors":"Sandrine Chausson, Marion Fourcade, David J. Harding, Björn Ross, Grégory Renard","doi":"10.1177/00491241251326819","DOIUrl":"https://doi.org/10.1177/00491241251326819","url":null,"abstract":"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.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"1 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851025","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

Locating Cultural Holes Brokers in Diffusion Dynamics Across Bright Symbolic Boundaries 在跨越明亮符号边界的扩散动力学中定位文化漏洞经纪人

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-03-19 DOI: 10.1177/00491241251322517

Diego F. Leal

引用次数: 0

Deep Learning With DAGs 利用 DAG 进行深度学习

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-03-12 DOI: 10.1177/00491241251319291

Sourabh Balgi, Adel Daoud, Jose M. Peña, Geoffrey T. Wodtke, Jesse Zhou

{"title":"Deep Learning With DAGs","authors":"Sourabh Balgi, Adel Daoud, Jose M. Peña, Geoffrey T. Wodtke, Jesse Zhou","doi":"10.1177/00491241251319291","DOIUrl":"https://doi.org/10.1177/00491241251319291","url":null,"abstract":"Social science theories often postulate systems of causal relationships among variables, which are commonly represented using directed acyclic graphs (DAGs). As non-parametric causal models, DAGs require no assumptions about the functional form of the hypothesized relationships. Nevertheless, to simplify empirical evaluation, researchers typically invoke such assumptions anyway, even though they are often arbitrary and do not reflect any theoretical content or prior knowledge. Moreover, functional form assumptions can engender bias, whenever they fail to accurately capture the true complexity of the system. In this article, we introduce causal-graphical normalizing flows (cGNFs), a novel approach to causal inference that leverages deep neural networks to empirically evaluate theories represented as DAGs. Unlike conventional methods, cGNFs model the full joint distribution of the data using a DAG specified by the analyst, without relying on stringent assumptions about functional form. This enables flexible, non-parametric estimation of any causal estimand identified from the DAG, including total effects, direct and indirect effects, and path-specific effects. We illustrate the method with a reanalysis of Blau and Duncan’s ( 1967 ) model of status attainment and Zhou’s ( 2019 ) model of controlled mobility. The article concludes with a discussion of current limitations and directions for future development.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"11 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143608045","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

When to Use Counterfactuals in Causal Historiography: Methods for Semantics and Inference 在因果史学中何时使用反事实：语义学和推理方法

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-02-03 DOI: 10.1177/00491241251314039

Tay Jeong

引用次数: 0

The Integration of Bayesian Regression Analysis and Bayesian Process Tracing in Mixed-Methods Research 混合方法研究中贝叶斯回归分析与贝叶斯过程跟踪的集成

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-01-23 DOI: 10.1177/00491241241295336

Lion Behrens, Ingo Rohlfing

引用次数: 0

Improving Cross-Cultural Comparability of Measures on Gender and Age Stereotypes by Means of Piloting Methods 通过试点方法提高性别和年龄刻板印象测量的跨文化可比性

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-01-21 DOI: 10.1177/00491241241307600

Natalja Menold, Patricia Hadler, Cornelia Neuert

引用次数: 0

The Rise in Occupational Coding Mismatches and Occupational Mobility, 1991–2020 1991-2020 年职业编码错配和职业流动性的上升

IF 6.3 2区社会学

Sociological Methods & Research Pub Date : 2025-01-16 DOI: 10.1177/00491241241303517

Andrew Taeho Kim, ChangHwan Kim

引用次数: 0