@conference {
    orvium-643f1aa90f649f6042841876,
    title = "Field Effects in Predicting Exceptional Growth in Research Communities",
    abstract = "Using a model of the literature indexed in Scopus, we have increased the accuracy of our ability to predict which of 20,747 research communities would achieve exceptional growth from 32.2 to 39.6 using double exponential smoothing of inertial indicators and by doing predictions in each of 26 fields rather than across the entire model. Each field nominated two (out of a possible 123) indicators as ‘best predictors’ following the procedure described in our previous studies. Significant diversity was found in which indicators performed best in each field, suggesting that field effects should be accounted for in predictive analytics. Nevertheless, there were groupings of contiguous fields with a surprising level of homogeneity in predictive indicators. Possible reasons for the similarities and differences are discussed.",
    keywords = "global model, accuracy, growth prediction, field effects",
    author = "Richard Klavans and Kevin Boyack and Caleb Smith",
    year = "2023",
    doi = "10.55835/643f1aa90f649f6042841876",
    language = "English",
    url = "https://dapp.orvium.io/deposits/643f1aa90f649f6042841876/view",
}