Docker files

This repository contains the replication material for the article "Schulte-Cloos (2021): 'Political potentials, deep-seated nativism and the success of the German AfD' in: Frontiers of Political Science, DOI: 10.3389/fpos.2021.698085" ⭐ The R Markdown file 'SCHULTE-CLOOS_Potentials_AfD_REPLICATION.Rmd' contains all code to reproduce the results. ⭐ The HTML file 'SCHULTE-CLOOS_Potentials_AfD_REPLICATION.html' is the rendered output of the R Markdown file. It contains all figures and tables reported in the main article and in the supplemental material accompanying the published manuscript. 💡 You can also inspect the code by activating the option Show All Code on the top right of the HTML file. ⭐ Reproducible environment: The Dockerfile allows you to build the Docker image used to run all analyses. Please ensure you have Docker installed. 💡 The R environment uses an MRAN snapshot of 2021-04-16 and relies on R version 4.0.3.

This repository contains the replication material for the article "Schulte-Cloos and Anghel (2023) 'Right-wing authoritarian attitudes, fast-paced decision-making, and the spread of misinformation about COVID-19 vaccines' in: Political Communication, DOI: 10.1080/10584609.2023.2291538". ⭐ The Quarto file 'Schulte-Cloos_Fast-Paced_Fake-News_Replication.qmd' contains all code to reproduce the results. ⭐The HTML file 'Schulte-Cloos_Fast-Paced_Fake-News_Replication.html' is the rendered output of the Quarto file. It contains all figures and tables reported in the main article and the supplemental material accompanying the published manuscript. ⭐ Reproducible environment: The Dockerfile allows you to build the Docker image used to run all analyses. Please ensure you have Docker installed.

This repository contains a replication archive for the article: Griswold, Robbins, and Pollard. (2025). "Stay Tuned: Improving Sentiment Analysis and Stance Detection Using Large Language Models". Political Analysis. Consolidates project material, which includes: replication instructions, project code, a shell script to replicate the article, input and output data, logs from a replication run, a docker image, and replication figures and tables.

This repository contains the replication material for the article "Anghel and Schulte-Cloos (2022): 'COVID-19 related anxieties do not decrease support for liberal democracy' in: European Journal of Political Research, DOI: 10.1111/1475-6765.12554" ⭐ The R Markdown file 'Schulte-Cloos_Covid-19 Anxieties_CEE_Replication.Rmd' contains all code to reproduce the results. ⭐ The HTML file 'Schulte-Cloos_Covid-19 Anxieties_CEE_Replication.html' is the rendered output of the R Markdown file. It contains all figures and tables reported in the main article and the supplemental material accompanying the published manuscript. 💡 You can also inspect the code by activating the option Show All Code on the top right of the HTML file. ⭐ Reproducible environment: The Dockerfile allows you to build the Docker image used to run all analyses. Please ensure you have Docker installed. 💡 The R environment uses an MRAN snapshot of 2022-07-05 and relies on R version 4.1.3.

For use at the JuliaEO2023, Global Workshop on Earth Observation with Julia, workshop. - Regional subsets of sea level & topography data for the region of the Azores. - Docker image for attendees & others to reproduce workshop notebook results. Workshop website is https://aircentre.github.io/JuliaEO/

Many enduring questions in international relations theory focus on power relations, so it is important that scholars have a good measure of relative power. The standard measure of relative military power, the capability ratio, is barely better than random guessing at pre- dicting militarized dispute outcomes. We use machine learning to build a superior proxy, the Dispute Outcome Expectations score, from the same underlying data. Our measure is an order of magnitude better than the capability ratio at predicting dispute outcomes. We replicate Reed et al. (2008) and find, contrary to the original conclusions, that the probability of conflict is always highest when the state with the least benefits has a preponderance of power. In replications of 18 other dyadic analyses that use power as a control, we find that replacing the standard measure with DOE scores usually improves both in-sample and out-of-sample goodness of fit. Note:This analysis involves many layers of computation: multiple imputation of the underlying data, creation of an ensemble of machine learning models on the imputed datasets, predictions from that ensemble, and replications of previous studies using those predictions. Our replication code sets seeds in any script where random numbers are drawn, and runs in a Docker environment to ensure identical package versions across machines. Nevertheless, because of differences in machine precision and floating point computations across CPUs, the replication code may not produce results identical to those in the paper. Any differences should be small in magnitude and should not affect any substantive conclusions of the analysis.