Record Details

Replication Data for: When Correlation Is Not Enough: Validating Populism Scores from Supervised Machine-Learning Models

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

View Archive Info
 
 
Field Value
 
Title Replication Data for: When Correlation Is Not Enough: Validating Populism Scores from Supervised Machine-Learning Models
 
Identifier https://doi.org/10.7910/DVN/DDXRXI
 
Creator Jankowski, Michael
Huber, Robert A.
 
Publisher Harvard Dataverse
 
Description Despite the ongoing success of populist parties in many parts of the world, we lack comprehensive information about parties' level of populism over time. A recent contribution to Political Analysis by Di Cocco and Monechi (DCM) suggests that this research gap can be closed by predicting parties' populism scores from their election manifestos using supervised machine-learning. In this paper, we provide a detailed discussion of the suggested approach. Building on recent debates about the validation of machine-learning models, we argue that the validity checks provided in DCM's paper are insufficient. We conduct a series of additional validity checks and empirically demonstrate that the approach is not suitable for deriving populism scores from texts. We conclude that measuring populism over time and between countries remains an immense challenge for empirical research. More generally, our paper illustrates the importance of more comprehensive validations of supervised machine-learning models.
 
Subject Social Sciences
 
Contributor Jankowski, Michael