Replication Data for: An Informed Forensics Approach to Detecting Vote Irregularities
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Electoral forensics involves examining election results for anomalies in order to efficiently identify patterns indicative of electoral irregularities.  However, there is disagreement about which, if any, forensics tool is most effective at identifying fraud, and there is no method for integrating multiple tools. Moreover, forensic efforts have failed to systematically take advantage of country-specific details that might aid in diagnosing fraud. We deploy a Bayesian additive regression trees (BART) model -- a machine-learning technique -- on a large cross-national dataset to explore the dense network of potential relationships between various forensic indicators of anomalies and electoral fraud risk factors, on the one hand, and the likelihood of fraud, on the other. This approach allows us to arbitrate between the relative importanc' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Bayesian additive regression trees","Election fraud","Electoral forensics","Machine learning"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Electoral forensics involves examining election results for anomalies in order to efficiently identify patterns indicative of electoral irregularities.  However, there is disagreement about which, if any, forensics tool is most effective at identifying fraud, and there is no method for integrating multiple tools. Moreover, forensic efforts have failed to systematically take advantage of country-specific details that might aid in diagnosing fraud. We deploy a Bayesian additive regression trees (BART) model -- a machine-learning technique -- on a large cross-national dataset to explore the dense network of potential relationships between various forensic indicators of anomalies and electoral fraud risk factors, on the one hand, and the likelihood of fraud, on the other. This approach allows us to arbitrate between the relative importanc' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Electoral forensics involves examining election results for anomalies in order to efficiently identify patterns indicative of electoral irregularities.  However, there is disagreement about which, if any, forensics tool is most effective at identifying fraud, and there is no method for integrating multiple tools. Moreover, forensic efforts have failed to systematically take advantage of country-specific details that might aid in diagnosing fraud. We deploy a Bayesian additive regression trees (BART) model -- a machine-learning technique -- on a large cross-national dataset to explore the dense network of potential relationships between various forensic indicators of anomalies and electoral fraud risk factors, on the one hand, and the likelihood of fraud, on the other. This approach allows us to arbitrate between the relative importanc
Bayesian additive regression trees; Election fraud; Electoral forensics; Machine learning