Computational Reproducibility in Computational Social Science

TL;DR

This paper discusses the importance of computational reproducibility in social science, proposing a tiered system to enhance verification levels and addressing barriers to achieving high reproducibility.

Contribution

It introduces a tier system for reproducibility in computational social science and offers solutions to overcome barriers to higher reproducibility levels.

Findings

Proposes a tiered reproducibility framework for computational social science.

Identifies barriers to achieving high reproducibility levels.

Suggests proactive strategies and alternative data sources for reproducibility.

Abstract

Replication crises have shaken the scientific landscape during the last decade. As potential solutions, open science practices were heavily discussed and have been implemented with varying success in different disciplines. We argue that computational-x disciplines such as computational social science, are also susceptible for the symptoms of the crises, but in terms of reproducibility. We expand the binary definition of reproducibility into a tier system which allows increasing levels of reproducibility based on external verfiability to counteract the practice of open-washing. We provide solutions for barriers in Computational Social Science that hinder researchers from obtaining the highest level of reproducibility, including the use of alternate data sources and considering reproducibility proactively.