The case for caution in predicting scientists' future impact

TL;DR

This study critically evaluates the predictive power of a model for scientists' future impact, revealing significant limitations especially for early career stages, which raises concerns about its practical application.

Contribution

The paper tests the existing career impact prediction model on real longitudinal data and highlights its reduced accuracy for early career predictions.

Findings

Model predicts h-index up to 6 years ahead when aggregating all ages.

Predictive accuracy drops significantly for early career stages.

Early career impact predictions are unreliable, limiting practical use.

Abstract

We stress-test the career predictability model proposed by Acuna et al. [Nature 489, 201-202 2012] by applying their model to a longitudinal career data set of 100 Assistant professors in physics, two from each of the top 50 physics departments in the US. The Acuna model claims to predict h(t+\Delta t), a scientist's h-index \Delta t years into the future, using a linear combination of 5 cumulative career measures taken at career age t. Here we investigate how the "predictability" depends on the aggregation of career data across multiple age cohorts. We confirm that the Acuna model does a respectable job of predicting h(t+\Delta t) up to roughly 6 years into the future when aggregating all age cohorts together. However, when calculated using subsets of specific age cohorts (e.g. using data for only t=3), we find that the model's predictive power significantly decreases, especially when applied to early career years. For young careers, the model does a much worse job of predicting future impact, and hence, exposes a serious limitation. The limitation is particularly concerning as early career decisions make up a significant portion, if not the majority, of cases where quantitative approaches are likely to be applied.