Analyzing the relationship between text features and research proposal productivity

TL;DR

This study explores how text features from research proposals' titles and abstracts relate to grant productivity, finding some predictive power but emphasizing the need for combined features for better accuracy.

Contribution

It introduces an analysis of text features' predictive ability for research grant productivity across multiple scientific fields, highlighting the limited but significant relationship.

Findings

Text length and lexical diversity are key predictors.

Prediction accuracy varies with project language.

Topical features outperform complexity measures.

Abstract

Predicting the output of research grants is of considerable relevance to research funding bodies, scientific entities and government agencies. In this study, we investigate whether text features extracted from projects title and abstracts are able to identify productive grants. Our analysis was conducted in three distinct areas, namely Medicine, Dentistry and Veterinary Medicine. Topical and complexity text features were used to identify predictors of productivity. The results indicate that there is a statistically significant relationship between text features and grants productivity, however such a dependence is weak. A feature relevance analysis revealed that the abstract text length and metrics derived from lexical diversity are among the most discriminative features. We also found that the prediction accuracy has a dependence on the considered project language and that topical features are more discriminative than text complexity measurements. Our findings suggest that text features should be used in combination with other features to assist the identification of relevant research ideas.