Political Analysis最新文献_第9页

PAN volume 30 issue 1 Cover and Back matter PAN第30卷第1期封面和封底

IF 5.4 2区社会学

Political Analysis Pub Date : 2022-01-01 DOI: 10.1017/pan.2021.45

引用次数: 0

Introduction to the Special Issue: Innovations and Current Challenges in Experimental Methods 特刊导论:实验方法的创新和当前挑战

IF 5.4 2区社会学

Political Analysis Pub Date : 2022-01-01 DOI: 10.1017/pan.2021.26

Libby Jenke

引用次数: 1

PAN volume 30 issue 1 Cover and Front matter PAN第30卷第1期封面和封面问题

IF 5.4 2区社会学

Political Analysis Pub Date : 2021-12-22 DOI: 10.1017/pan.2021.44

引用次数: 0

Human Rights Violations in Space: Assessing the External Validity of Machine-Geocoded versus Human-Geocoded Data 空间侵犯人权行为：评估机器地理编码数据与人类地理编码数据的外部有效性

IF 5.4 2区社会学

Political Analysis Pub Date : 2021-12-15 DOI: 10.1017/pan.2021.40

Logan Stundal, Benjamin E. Bagozzi, John R. Freeman, J. Holmes

{"title":"Human Rights Violations in Space: Assessing the External Validity of Machine-Geocoded versus Human-Geocoded Data","authors":"Logan Stundal, Benjamin E. Bagozzi, John R. Freeman, J. Holmes","doi":"10.1017/pan.2021.40","DOIUrl":"https://doi.org/10.1017/pan.2021.40","url":null,"abstract":"Abstract Political event data are widely used in studies of political violence. Recent years have seen notable advances in the automated coding of political event data from international news sources. Yet, the validity of machine-coded event data remains disputed, especially in the context of event geolocation. We analyze the frequencies of human- and machine-geocoded event data agreement in relation to an independent (ground truth) source. The events are human rights violations in Colombia. We perform our evaluation for a key, 8-year period of the Colombian conflict and in three 2-year subperiods as well as for a selected set of (non)journalistically remote municipalities. As a complement to this analysis, we estimate spatial probit models based on the three datasets. These models assume Gaussian Markov Random Field error processes; they are constructed using a stochastic partial differential equation and estimated with integrated nested Laplacian approximation. The estimated models tell us whether the three datasets produce comparable predictions, underreport events in relation to the same covariates, and have similar patterns of prediction error. Together the two analyses show that, for this subnational conflict, the machine- and human-geocoded datasets are comparable in terms of external validity but, according to the geostatistical models, produce prediction errors that differ in important respects.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46399224","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: 2

Rejoinder: Concluding Remarks on Scholarly Communications 复辩状：关于学术交流的结论性意见

IF 5.4 2区社会学

Political Analysis Pub Date : 2021-12-02 DOI: 10.1017/pan.2021.48

Jonathan Katz, G. King, E. Rosenblatt

引用次数: 0

The Essential Role of Statistical Inference in Evaluating Electoral Systems: A Response to DeFord et al. 统计推断在评估选举制度中的重要作用:对DeFord等人的回应。

IF 5.4 2区社会学

Political Analysis Pub Date : 2021-12-02 DOI: 10.1017/pan.2021.46

Jonathan N. Katz, Gary King, E. Rosenblatt

引用次数: 1

Implementing Partisan Symmetry: A Response to a Response 实现党派对称:对回应的回应

IF 5.4 2区社会学

Political Analysis Pub Date : 2021-12-02 DOI: 10.1017/pan.2021.47

Daryl R. DeFord, Natasha Dhamankar, M. Duchin, Varun Gupta, Mackenzie McPike, Gabe Schoenbach, Ki Wan Sim

引用次数: 1

Minmaxing of Bayesian Improved Surname Geocoding and Geography Level Ups in Predicting Race 贝叶斯改进姓氏地理编码的最大化和种族预测中的地理层次提升

IF 5.4 2区社会学

Political Analysis Pub Date : 2021-11-29 DOI: 10.1017/pan.2021.31

Jesse T. Clark, John A. Curiel, T. Steelman

{"title":"Minmaxing of Bayesian Improved Surname Geocoding and Geography Level Ups in Predicting Race","authors":"Jesse T. Clark, John A. Curiel, T. Steelman","doi":"10.1017/pan.2021.31","DOIUrl":"https://doi.org/10.1017/pan.2021.31","url":null,"abstract":"Abstract Racial identification is a critical factor in understanding a multitude of important outcomes in many fields. However, inferring an individual’s race from ecological data is prone to bias and error. This process was only recently improved via Bayesian improved surname geocoding (BISG). With surname and geographic-based demographic data, it is possible to more accurately estimate individual racial identification than ever before. However, the level of geography used in this process varies widely. Whereas some existing work makes use of geocoding to place individuals in precise census blocks, a substantial portion either skips geocoding altogether or relies on estimation using surname or county-level analyses. Presently, the trade-offs of such variation are unknown. In this letter, we quantify those trade-offs through a validation of BISG on Georgia’s voter file using both geocoded and nongeocoded processes and introduce a new level of geography—ZIP codes—to this method. We find that when estimating the racial identification of White and Black voters, nongeocoded ZIP code-based estimates are acceptable alternatives. However, census blocks provide the most accurate estimations when imputing racial identification for Asian and Hispanic voters. Our results document the most efficient means to sequentially conduct BISG analysis to maximize racial identification estimation while simultaneously minimizing data missingness and bias.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46254036","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: 5

Cross-Domain Topic Classification for Political Texts 政治文本的跨领域主题分类

IF 5.4 2区社会学

Political Analysis Pub Date : 2021-10-21 DOI: 10.1017/pan.2021.37

Moritz Osnabrügge, Elliott Ash, M. Morelli

{"title":"Cross-Domain Topic Classification for Political Texts","authors":"Moritz Osnabrügge, Elliott Ash, M. Morelli","doi":"10.1017/pan.2021.37","DOIUrl":"https://doi.org/10.1017/pan.2021.37","url":null,"abstract":"Abstract We introduce and assess the use of supervised learning in cross-domain topic classification. In this approach, an algorithm learns to classify topics in a labeled source corpus and then extrapolates topics in an unlabeled target corpus from another domain. The ability to use existing training data makes this method significantly more efficient than within-domain supervised learning. It also has three advantages over unsupervised topic models: the method can be more specifically targeted to a research question and the resulting topics are easier to validate and interpret. We demonstrate the method using the case of labeled party platforms (source corpus) and unlabeled parliamentary speeches (target corpus). In addition to the standard within-domain error metrics, we further validate the cross-domain performance by labeling a subset of target-corpus documents. We find that the classifier accurately assigns topics in the parliamentary speeches, although accuracy varies substantially by topic. We also propose tools diagnosing cross-domain classification. To illustrate the usefulness of the method, we present two case studies on how electoral rules and the gender of parliamentarians influence the choice of speech topics.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46636031","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: 17

How Populist are Parties? Measuring Degrees of Populism in Party Manifestos Using Supervised Machine Learning 政党有多民粹主义？用有监督的机器学习测量政党宣言中的民粹主义程度

IF 5.4 2区社会学

Political Analysis Pub Date : 2021-10-15 DOI: 10.1017/pan.2021.29

Jessica Di Cocco, Bernardo Monechi

引用次数: 15