Political Analysis最新文献_第3页

Relatio: Text Semantics Capture Political and Economic Narratives – ERRATUM 关系:文本语义捕捉政治和经济叙事-勘误

2区社会学

Political Analysis Pub Date : 2023-05-19 DOI: 10.1017/pan.2023.15

Elliott Ash, Germain Gauthier, Philine Widmer

引用次数: 0

The Role of Majority Status in Close Election Studies 多数地位在势均力敌的选举研究中的作用

2区社会学

Political Analysis Pub Date : 2023-04-18 DOI: 10.1017/pan.2023.14

Matteo Alpino, Marta Crispino

引用次数: 0

Simulating Party Shares 模拟各方股份

IF 5.4 2区社会学

Political Analysis Pub Date : 2023-04-11 DOI: 10.1017/pan.2023.13

D. Cohen, Chris Hanretty

引用次数: 0

PAN volume 31 issue 2 Cover and Front matter PAN第31卷第2期封面和封面问题

IF 5.4 2区社会学

Political Analysis Pub Date : 2023-04-01 DOI: 10.1017/pan.2023.10

引用次数: 0

PAN volume 31 issue 2 Cover and Back matter PAN第31卷第2期封面和封底

IF 5.4 2区社会学

Political Analysis Pub Date : 2023-04-01 DOI: 10.1017/pan.2023.11

引用次数: 0

It’s All in the Name: A Character-Based Approach to Infer Religion 这一切都在名字里:一种基于人物的方法来推断宗教

2区社会学

Political Analysis Pub Date : 2023-03-23 DOI: 10.1017/pan.2023.6

Rochana Chaturvedi, Sugat Chaturvedi

引用次数: 1

Integrating Data Across Misaligned Spatial Units 跨错位空间单元集成数据

IF 5.4 2区社会学

Political Analysis Pub Date : 2023-03-23 DOI: 10.1017/pan.2023.5

Y. Zhukov, Jason S. Byers, Marty Davidson, Ken Kollman

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引用次数: 1

Introducing an Interpretable Deep Learning Approach to Domain-Specific Dictionary Creation: A Use Case for Conflict Prediction 引入一种可解释的深度学习方法来创建特定领域的词典：冲突预测的一个用例

IF 5.4 2区社会学

Political Analysis Pub Date : 2023-03-22 DOI: 10.1017/pan.2023.7

Sonja Häffner, Martin Hofer, Maximilian Nagl, Julian Walterskirchen

{"title":"Introducing an Interpretable Deep Learning Approach to Domain-Specific Dictionary Creation: A Use Case for Conflict Prediction","authors":"Sonja Häffner, Martin Hofer, Maximilian Nagl, Julian Walterskirchen","doi":"10.1017/pan.2023.7","DOIUrl":"https://doi.org/10.1017/pan.2023.7","url":null,"abstract":"Abstract Recent advancements in natural language processing (NLP) methods have significantly improved their performance. However, more complex NLP models are more difficult to interpret and computationally expensive. Therefore, we propose an approach to dictionary creation that carefully balances the trade-off between complexity and interpretability. This approach combines a deep neural network architecture with techniques to improve model explainability to automatically build a domain-specific dictionary. As an illustrative use case of our approach, we create an objective dictionary that can infer conflict intensity from text data. We train the neural networks on a corpus of conflict reports and match them with conflict event data. This corpus consists of over 14,000 expert-written International Crisis Group (ICG) CrisisWatch reports between 2003 and 2021. Sensitivity analysis is used to extract the weighted words from the neural network to build the dictionary. In order to evaluate our approach, we compare our results to state-of-the-art deep learning language models, text-scaling methods, as well as standard, nonspecialized, and conflict event dictionary approaches. We are able to show that our approach outperforms other approaches while retaining interpretability.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":"31 1","pages":"481 - 499"},"PeriodicalIF":5.4,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44604107","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: 4

Mapping Literature with Networks: An Application to Redistricting 用网络绘制文献:在重划中的应用

IF 5.4 2区社会学

Political Analysis Pub Date : 2023-03-21 DOI: 10.1017/pan.2023.4

Adeline Lo, Devin Judge-Lord, Kyler Hudson, Kenneth R. Mayer

{"title":"Mapping Literature with Networks: An Application to Redistricting","authors":"Adeline Lo, Devin Judge-Lord, Kyler Hudson, Kenneth R. Mayer","doi":"10.1017/pan.2023.4","DOIUrl":"https://doi.org/10.1017/pan.2023.4","url":null,"abstract":"Abstract Understanding the gaps and connections across existing theories and findings is a perennial challenge in scientific research. Systematically reviewing scholarship is especially challenging for researchers who may lack domain expertise, including junior scholars or those exploring new substantive territory. Conversely, senior scholars may rely on long-standing assumptions and social networks that exclude new research. In both cases, ad hoc literature reviews hinder accumulation of knowledge. Scholars are rarely systematic in selecting relevant prior work or then identifying patterns across their sample. To encourage systematic, replicable, and transparent methods for assessing literature, we propose an accessible network-based framework for reviewing scholarship. In our method, we consider a literature as a network of recurring concepts (nodes) and theorized relationships among them (edges). Network statistics and visualization allow researchers to see patterns and offer reproducible characterizations of assertions about the major themes in existing literature. Critically, our approach is systematic and powerful but also low cost; it requires researchers to enter relationships they observe in prior studies into a simple spreadsheet—a task accessible to new and experienced researchers alike. Our open-source R package enables researchers to leverage powerful network analysis while minimizing software-specific knowledge. We demonstrate this approach by reviewing redistricting literature.","PeriodicalId":48270,"journal":{"name":"Political Analysis","volume":"31 1","pages":"669 - 678"},"PeriodicalIF":5.4,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43203141","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

Topic Classification for Political Texts with Pretrained Language Models 基于预训练语言模型的政治文本主题分类

IF 5.4 2区社会学

Political Analysis Pub Date : 2023-03-08 DOI: 10.1017/pan.2023.3

Yu Wang

引用次数: 0