Pub Date : 2022-11-28DOI: 10.1177/00491241221140426
John Levi Martin
Small and Calarco have done the field a great service; we must go further and arm readers with better understandings of when authors have in fact fulfilled Small and Calarco’s strictures.
{"title":"Cognitive Plausibility and Qualitative Research","authors":"John Levi Martin","doi":"10.1177/00491241221140426","DOIUrl":"https://doi.org/10.1177/00491241221140426","url":null,"abstract":"Small and Calarco have done the field a great service; we must go further and arm readers with better understandings of when authors have in fact fulfilled Small and Calarco’s strictures.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"52 1","pages":"1048 - 1058"},"PeriodicalIF":6.3,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47087547","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}
Pub Date : 2022-11-24DOI: 10.1177/00491241221134523
Rosa W. Runhardt
This article uses the interventionist theory of causation, a counterfactual theory taken from philosophy of science, to strengthen causal analysis in process tracing research. Causal claims from pr...
本文运用科学哲学中的反事实理论——干涉主义因果理论，在过程追溯研究中加强因果分析。从pr…
{"title":"Concrete Counterfactual Tests for Process Tracing: Defending an Interventionist Potential Outcomes Framework","authors":"Rosa W. Runhardt","doi":"10.1177/00491241221134523","DOIUrl":"https://doi.org/10.1177/00491241221134523","url":null,"abstract":"This article uses the interventionist theory of causation, a counterfactual theory taken from philosophy of science, to strengthen causal analysis in process tracing research. Causal claims from pr...","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"53 8","pages":""},"PeriodicalIF":6.3,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50167748","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}
Pub Date : 2022-11-11DOI: 10.1177/00491241221134522
O. Smallenbroek, F. Hertel, C. Barone
In social stratification research, the most frequently used social class schema are based on employment relations (EGP and ESEC). These schemes have been propelled to paradigms for research on soci...
{"title":"Measuring Class Hierarchies in Postindustrial Societies: A Criterion and Construct Validation of EGP and ESEC Across 31 Countries","authors":"O. Smallenbroek, F. Hertel, C. Barone","doi":"10.1177/00491241221134522","DOIUrl":"https://doi.org/10.1177/00491241221134522","url":null,"abstract":"In social stratification research, the most frequently used social class schema are based on employment relations (EGP and ESEC). These schemes have been propelled to paradigms for research on soci...","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"52 10","pages":""},"PeriodicalIF":6.3,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50167753","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}
Pub Date : 2022-11-01Epub Date: 2022-12-02DOI: 10.1177/00491241221122603
Alina Arseniev-Koehler, Jacob G Foster
Public culture is a powerful source of cognitive socialization; for example, media language is full of meanings about body weight. Yet it remains unclear how individuals process meanings in public culture. We suggest that schema learning is a core mechanism by which public culture becomes personal culture. We propose that a burgeoning approach in computational text analysis - neural word embeddings - can be interpreted as a formal model for cultural learning. Embeddings allow us to empirically model schema learning and activation from natural language data. We illustrate our approach by extracting four lower-order schemas from news articles: the gender, moral, health, and class meanings of body weight. Using these lower-order schemas we quantify how words about body weight "fill in the blanks" about gender, morality, health, and class. Our findings reinforce ongoing concerns that machine-learning models (e.g., of natural language) can encode and reproduce harmful human biases.
{"title":"Machine Learning as a Model for Cultural Learning: Teaching an Algorithm What it Means to be Fat.","authors":"Alina Arseniev-Koehler, Jacob G Foster","doi":"10.1177/00491241221122603","DOIUrl":"10.1177/00491241221122603","url":null,"abstract":"<p><p>Public culture is a powerful source of cognitive socialization; for example, media language is full of meanings about body weight. Yet it remains unclear how individuals process meanings in public culture. We suggest that schema learning is a core mechanism by which public culture becomes personal culture. We propose that a burgeoning approach in computational text analysis - neural word embeddings - can be interpreted as a formal model for cultural learning. Embeddings allow us to empirically model schema learning and activation from natural language data. We illustrate our approach by extracting four lower-order schemas from news articles: the gender, moral, health, and class meanings of body weight. Using these lower-order schemas we quantify how words about body weight \"fill in the blanks\" about gender, morality, health, and class. Our findings reinforce ongoing concerns that machine-learning models (e.g., of natural language) can encode and reproduce harmful human biases.</p>","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"51 1","pages":"1484-1539"},"PeriodicalIF":6.3,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10653277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45938371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1177/00491241221123088
Bart Bonikowski, Laura K. Nelson
As the field of computational text analysis within the social sciences is maturing, computational methods are no longer seen as ends in themselves, but rather as means toward answering theoretically motivated research questions. The objective of this special issue is to showcase such research: the use of novel computational methods in the service of advancing substantive scientific knowledge. In presenting the contributions to the issue, we discuss several insights that emerge from this work, which hold relevance not only for current and aspiring practitioners of computational text analysis, but also for its skeptics. These concern the central role of theory in designing and executing computational research, the selection of appropriate techniques from a rapidly growing methodological toolkit, the benefits—and risks—of methodological bricolage, and the necessity of validating all aspects of the research process. The result is a set of broad considerations concerning the effective application of computational methods to substantive questions, illustrated by eight exemplary empirical studies.
{"title":"From Ends to Means: The Promise of Computational Text Analysis for Theoretically Driven Sociological Research","authors":"Bart Bonikowski, Laura K. Nelson","doi":"10.1177/00491241221123088","DOIUrl":"https://doi.org/10.1177/00491241221123088","url":null,"abstract":"As the field of computational text analysis within the social sciences is maturing, computational methods are no longer seen as ends in themselves, but rather as means toward answering theoretically motivated research questions. The objective of this special issue is to showcase such research: the use of novel computational methods in the service of advancing substantive scientific knowledge. In presenting the contributions to the issue, we discuss several insights that emerge from this work, which hold relevance not only for current and aspiring practitioners of computational text analysis, but also for its skeptics. These concern the central role of theory in designing and executing computational research, the selection of appropriate techniques from a rapidly growing methodological toolkit, the benefits—and risks—of methodological bricolage, and the necessity of validating all aspects of the research process. The result is a set of broad considerations concerning the effective application of computational methods to substantive questions, illustrated by eight exemplary empirical studies.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"51 1","pages":"1469 - 1483"},"PeriodicalIF":6.3,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47988770","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}
Pub Date : 2022-11-01DOI: 10.1177/00491241221122317
Bart Bonikowski, Yuchen Luo, Oscar Stuhler
Radical-right campaigns commonly employ three discursive elements: anti-elite populism, exclusionary and declinist nationalism, and authoritarianism. Recent scholarship has explored whether these frames have diffused from radical-right to centrist parties in the latter’s effort to compete for the former’s voters. This study instead investigates whether similar frames had been used by mainstream political actors prior to their exploitation by the radical right (in the U.S., Donald Trump’s 2016 and 2020 campaigns). To do so, we identify instances of populism, nationalism (i.e., exclusionary and inclusive definitions of national symbolic boundaries and displays of low and high national pride), and authoritarianism in the speeches of Democratic and Republican presidential nominees between 1952 and 2020. These frames are subtle, infrequent, and polysemic, which makes their measurement difficult. We overcome this by leveraging the affordances of neural language models—in particular, a robustly optimized variant of bidirectional encoder representations from Transformers (RoBERTa) and active learning. As we demonstrate, this approach is more effective for measuring discursive frames than other methods commonly used by social scientists. Our results suggest that what set Donald Trump’s campaign apart from those of mainstream presidential candidates was not the invention of a new form of politics, but the combination of negative evaluations of elites, low national pride, and authoritarianism—all of which had long been present among both parties—with an explicit evocation of exclusionary nationalism, which had been articulated only implicitly by prior presidential nominees. Radical-right discourse—at least at the presidential level in the United States—should therefore be characterized not as a break with the past but as an amplification and creative rearrangement of existing political-cultural tropes.
{"title":"Politics as Usual? Measuring Populism, Nationalism, and Authoritarianism in U.S. Presidential Campaigns (1952–2020) with Neural Language Models","authors":"Bart Bonikowski, Yuchen Luo, Oscar Stuhler","doi":"10.1177/00491241221122317","DOIUrl":"https://doi.org/10.1177/00491241221122317","url":null,"abstract":"Radical-right campaigns commonly employ three discursive elements: anti-elite populism, exclusionary and declinist nationalism, and authoritarianism. Recent scholarship has explored whether these frames have diffused from radical-right to centrist parties in the latter’s effort to compete for the former’s voters. This study instead investigates whether similar frames had been used by mainstream political actors prior to their exploitation by the radical right (in the U.S., Donald Trump’s 2016 and 2020 campaigns). To do so, we identify instances of populism, nationalism (i.e., exclusionary and inclusive definitions of national symbolic boundaries and displays of low and high national pride), and authoritarianism in the speeches of Democratic and Republican presidential nominees between 1952 and 2020. These frames are subtle, infrequent, and polysemic, which makes their measurement difficult. We overcome this by leveraging the affordances of neural language models—in particular, a robustly optimized variant of bidirectional encoder representations from Transformers (RoBERTa) and active learning. As we demonstrate, this approach is more effective for measuring discursive frames than other methods commonly used by social scientists. Our results suggest that what set Donald Trump’s campaign apart from those of mainstream presidential candidates was not the invention of a new form of politics, but the combination of negative evaluations of elites, low national pride, and authoritarianism—all of which had long been present among both parties—with an explicit evocation of exclusionary nationalism, which had been articulated only implicitly by prior presidential nominees. Radical-right discourse—at least at the presidential level in the United States—should therefore be characterized not as a break with the past but as an amplification and creative rearrangement of existing political-cultural tropes.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"51 1","pages":"1721 - 1787"},"PeriodicalIF":6.3,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46554884","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}
Pub Date : 2022-11-01DOI: 10.1177/00491241221122528
C. Knight
Research in organizational theory takes as a key premise the notion that organizations are “actors.” Organizational actorhood, or agency, depends, in part, on how external audiences perceive organizations. In other words, organizational agency requires that external audiences take organizations to be agents. Yet little empirical research has attempted to measure these attributions: when do audiences assume that organizations are agents and how have these attributions changed over time? In this article, I suggest that scholars can triangulate across computational methods—including named entity recognition, dependency parsing, topic models, and dictionary methods—to analyze attributions of agency in text, discourse that I term “agent talk.” I demonstrate the utility of this approach by analyzing how business organizations were discussed as agents during a key period of organizational development, the turn of the twentieth century. Analyzing articles from two of the leading national newspapers, the Wall Street Journal and New York Times, I examine agent talk in everyday business discourse. I find that agent talk generally increased over the early twentieth century, as organizations were depicted as active subjects in text and personified as speakers. Moreover, I find that this discourse was concentrated in social and legal semantic contexts: in particular, contexts relating to labor, regulation, and railroads. Finally, I show the uneven growth of this rhetoric over time, as organizations across different semantic arenas were personified as speakers. Overall, these results show how measures of discourse can provide a window into how and when audiences endow organizations with actorhood.
{"title":"When Corporations Are People: Agent Talk and the Development of Organizational Actorhood, 1890–1934","authors":"C. Knight","doi":"10.1177/00491241221122528","DOIUrl":"https://doi.org/10.1177/00491241221122528","url":null,"abstract":"Research in organizational theory takes as a key premise the notion that organizations are “actors.” Organizational actorhood, or agency, depends, in part, on how external audiences perceive organizations. In other words, organizational agency requires that external audiences take organizations to be agents. Yet little empirical research has attempted to measure these attributions: when do audiences assume that organizations are agents and how have these attributions changed over time? In this article, I suggest that scholars can triangulate across computational methods—including named entity recognition, dependency parsing, topic models, and dictionary methods—to analyze attributions of agency in text, discourse that I term “agent talk.” I demonstrate the utility of this approach by analyzing how business organizations were discussed as agents during a key period of organizational development, the turn of the twentieth century. Analyzing articles from two of the leading national newspapers, the Wall Street Journal and New York Times, I examine agent talk in everyday business discourse. I find that agent talk generally increased over the early twentieth century, as organizations were depicted as active subjects in text and personified as speakers. Moreover, I find that this discourse was concentrated in social and legal semantic contexts: in particular, contexts relating to labor, regulation, and railroads. Finally, I show the uneven growth of this rhetoric over time, as organizations across different semantic arenas were personified as speakers. Overall, these results show how measures of discourse can provide a window into how and when audiences endow organizations with actorhood.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"51 1","pages":"1634 - 1680"},"PeriodicalIF":6.3,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46933225","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}
Pub Date : 2022-10-15DOI: 10.1177/00491241221122596
Andrea Voyer, Zachary D. Kline, Madison Danton, Tatiana Volkova
This article presents a computational approach to examining immigrant incorporation through shifts in the social “mainstream.” Analyzing a historical corpus of American etiquette books, texts from 1922–2017 describing social norms, we identify mainstream shifts related to long-standing groups which once were and may currently still be seen as immigrant outsiders in the United States: Catholic, Chinese, Irish, Italian, Jewish, Mexican, and Muslim groups. The analysis takes a computational grounded theory approach, combining qualitative readings and computational text analyses. Using word embeddings, we operationalize the chosen groups as focal group concepts. We extract sections of text that are salient to the focal group concepts to create group-specific text corpora. Two computational approaches make it possible to examine mainstream shifts in these corpora. First, we use sentiment analysis to observe the positive sentiment in each corpus and its change over time. Second, we observe changes in each corpus's position on a semantic dimension represented by the poles of “strange” and “normal.” The results indicate mainstream shifts through increases in positive sentiment and movement from strange to normal over time for most of the group-specific corpora. These research techniques can be adapted to other studies of social sentiment and symbolic inclusion.
{"title":"From Strange to Normal: Computational Approaches to Examining Immigrant Incorporation Through Shifts in the Mainstream","authors":"Andrea Voyer, Zachary D. Kline, Madison Danton, Tatiana Volkova","doi":"10.1177/00491241221122596","DOIUrl":"https://doi.org/10.1177/00491241221122596","url":null,"abstract":"This article presents a computational approach to examining immigrant incorporation through shifts in the social “mainstream.” Analyzing a historical corpus of American etiquette books, texts from 1922–2017 describing social norms, we identify mainstream shifts related to long-standing groups which once were and may currently still be seen as immigrant outsiders in the United States: Catholic, Chinese, Irish, Italian, Jewish, Mexican, and Muslim groups. The analysis takes a computational grounded theory approach, combining qualitative readings and computational text analyses. Using word embeddings, we operationalize the chosen groups as focal group concepts. We extract sections of text that are salient to the focal group concepts to create group-specific text corpora. Two computational approaches make it possible to examine mainstream shifts in these corpora. First, we use sentiment analysis to observe the positive sentiment in each corpus and its change over time. Second, we observe changes in each corpus's position on a semantic dimension represented by the poles of “strange” and “normal.” The results indicate mainstream shifts through increases in positive sentiment and movement from strange to normal over time for most of the group-specific corpora. These research techniques can be adapted to other studies of social sentiment and symbolic inclusion.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"51 1","pages":"1540 - 1579"},"PeriodicalIF":6.3,"publicationDate":"2022-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43565259","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}
Pub Date : 2022-09-21DOI: 10.1177/00491241221122606
Weihua An
Egocentric networks represent a popular research design for network research. However, to what extent and under what conditions egocentric network centrality can serve as reasonable substitutes for...
{"title":"Comparing Egocentric and Sociocentric Centrality Measures in Directed Networks","authors":"Weihua An","doi":"10.1177/00491241221122606","DOIUrl":"https://doi.org/10.1177/00491241221122606","url":null,"abstract":"Egocentric networks represent a popular research design for network research. However, to what extent and under what conditions egocentric network centrality can serve as reasonable substitutes for...","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"50 15","pages":""},"PeriodicalIF":6.3,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50167915","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}
Pub Date : 2022-09-13DOI: 10.1177/00491241221122576
Nathaniel Josephs, Dennis M. Feehan, Forrest W. Crawford
The network scale-up method (NSUM) is a survey-based method for estimating the number of individuals in a hidden or hard-to-reach subgroup of a general population. In NSUM surveys, sampled individu...
{"title":"A Sample Size Formula for Network Scale-up Studies","authors":"Nathaniel Josephs, Dennis M. Feehan, Forrest W. Crawford","doi":"10.1177/00491241221122576","DOIUrl":"https://doi.org/10.1177/00491241221122576","url":null,"abstract":"The network scale-up method (NSUM) is a survey-based method for estimating the number of individuals in a hidden or hard-to-reach subgroup of a general population. In NSUM surveys, sampled individu...","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"50 12","pages":""},"PeriodicalIF":6.3,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50167918","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}
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