61 to 70 of 586 Results
Girbau, Andreu; Kobayashi, Tetsuro; Renoust, Benjami; Matsui, Yusuke; Satoh, Shin'ichi, 2023, "Replication Data for: Face Detection, Tracking, and Classification from Large-Scale News Archives for Analysis of Key Political Figures", https://doi.org/10.7910/DVN/TBWMCG, Harvard Dataverse, V1
Analyzing the appearances of political figures in large-scale news archives is increasingly important with the growing availability of large-scale news archives and developments in computer vision. We present a deep learning-based method combining face detection, tracking, and classification, which is particularly unique because it does not require... |
Rainey, Carlisle, 2023, "Replication Data for: "Hypothesis Tests Under Separation"", https://doi.org/10.7910/DVN/6EYRJG, Harvard Dataverse, V1
Separation commonly occurs in political science, usually when a binary explanatory variable perfectly predicts a binary outcome. In these situations, methodologists often recommend penalized maximum likelihood or Bayesian estimation. But researchers might struggle to identify an appropriate penalty or prior distribution. Fortunately, I show that re... |
Jul 27, 2023
Chang, Qing; Goplerud, Max, 2023, "Replication Data for: Generalized Kernel Regularized Least Squares", https://doi.org/10.7910/DVN/WNW0AD, Harvard Dataverse, V1
Kernel Regularized Least Squares (KRLS) is a popular method for flexibly estimating models that may have complex relationships between variables. However, its usefulness to many researchers is limited for two reasons. First, existing approaches are inflexible and do not allow KRLS to be combined with theoretically-motivated extensions such as rando... |
Jul 27, 2023
Torres, Michelle, 2023, "Replication Data for: A framework for the unsupervised analysis of images", https://doi.org/10.7910/DVN/PZYLYU, Harvard Dataverse, V1
This article introduces to political science a framework to analyze the content of visual material through unsupervised and semi-supervised methods. It details the implementation of a tool from the computer vision field, the Bag of Visual Words, for the definition and extraction of "tokens'' that allow researchers to build an Image-Visual Word matr... |
Jul 14, 2023
Carlson, Jacob; Incerti, Trevor; Aronow, P.M., 2023, "Replication Data for: Dyadic Clustering in International Relations", https://doi.org/10.7910/DVN/9I0LRQ, Harvard Dataverse, V1
Quantitative empirical inquiry in international relations often relies on dyadic data. Standard analytic techniques do not account for the fact that dyads are not generally independent of one another. That is, when dyads share a constituent member (e.g., a common country), they may be statistically dependent, or "clustered." Recent work has develop... |
Jun 20, 2023
Luechinger, Simon; Schelker, Mark; Schmid, Lukas, 2023, "Replication Data for Measuring Closeness in Proportional Representation Systems", https://doi.org/10.7910/DVN/FBZMWN, Harvard Dataverse, V1
We provide closed-form solutions for measuring electoral closeness of candidates in proportional representation systems. In contrast to plurality systems, closeness in proportional representation systems cannot be directly inferred from votes. Our measure captures electoral closeness for both open- and closed-list systems and for both main families... |
Hartmann, Felix; Humphreys, Macartan; Geissler, Ferdinand; Klüver, Heike; Giesecke, Johannes, 2023, "Replication Data for: Trading Liberties: Estimating Covid policy preferences from conjoint data", https://doi.org/10.7910/DVN/SVD9SV, Harvard Dataverse, V1, UNF:6:RJezlsbjfp8i1KUmNPivlQ== [fileUNF]
Survey experiments are an important tool to measure policy preferences. Researchers often rely on the random assignment of policy attribute levels to estimate different types of average marginal effects. Yet, researchers are often interested in how respondents trade-off different policy dimensions. We use a conjoint experiment administered to more... |
Ash, Elliott; Galletta, Sergio; Hangartner, Dominik; Margalit, Yotam; Pinna, Matteo, 2023, "Replication Data for: The Effect of Fox News on Health Behavior during COVID-19", https://doi.org/10.7910/DVN/ABOTUG, Harvard Dataverse, V1
In the early weeks of the 2020 coronavirus (COVID-19) pandemic, the Fox News Channel advanced a skeptical narrative that downplayed the risks posed by the virus. We find that this narrative had significant consequences: in localities with higher Fox News viewership-exogenous due to random variation in channel positioning-people were less likely to... |
Kaufman, Aaron, 2023, "Replication Data for: Selecting More Informative Training Sets with Fewer Observations", https://doi.org/10.7910/DVN/4ROL8S, Harvard Dataverse, V1
A standard text-as-data workflow in the social sciences involves identifying a set of documents to be labeled, selecting a random sample of them to label using research assistants, training a supervised learner to label the remaining documents, and validating that model’s performance using standard accuracy metrics. The most resource-intensive comp... |
Laurer, Moritz; van Atteveldt, Wouter; Casas, Andreu; Welbers, Kasper, 2023, "Replication Data for: Less Annotating, More Classifying: Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT-NLI", https://doi.org/10.7910/DVN/8ACDTT, Harvard Dataverse, V1, UNF:6:0t3Zupy3NJPRAGGOCZiAMg== [fileUNF]
Supervised machine learning is an increasingly popular tool for analysing large political text corpora. The main disadvantage of supervised machine learning is the need for thousands of manually annotated training data points. This issue is particularly important in the social sciences where most new research questions require the automation of a n... |
