Predicting publication productivity for researchers: a piecewise Poisson model

TL;DR

This paper introduces a piecewise Poisson model to predict researchers' publication productivity, validated on dblp data, aiding academic and funding decisions with unbiased, quantitative insights.

Contribution

The study presents a novel piecewise Poisson model for accurately predicting researcher productivity based on publication distribution features.

Findings

High prediction accuracy on dblp dataset

Effective in forecasting publication trends over time

Provides unbiased, quantitative evaluation for funding agencies

Abstract

Predicting the scientific productivity of researchers is a basic task for academic administrators and funding agencies. This study provided a model for the publication dynamics of researchers, inspired by the distribution feature of researchers' publications in quantity. It is a piecewise Poisson model, analyzing and predicting the publication productivity of researchers by regression. The principle of the model is built on the explanation for the distribution feature as a result of an inhomogeneous Poisson process that can be approximated as a piecewise Poisson process. The model's principle was validated by the high quality dblp dataset, and its effectiveness was testified in predicting the publication productivity for majority of researchers and the evolutionary trend of their publication productivity. Tests to confirm or disconfirm the model are also proposed. The model has the advantage of providing results in an unbiased way; thus is useful for funding agencies that evaluate a vast number of applications with a quantitative index on publications.