We stress-test the career predictability model proposed by Acuna et al. [Nature 489, 201-2 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 + ∆t), a scientist's h-index ∆t 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 + ∆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.
This methodology has been criticized since predictability depends on the aggregation of career data across multiple age cohorts, leading to models unfair towards younger researchers REF .
The case for caution in predicting scientists' future impact
Physics Today 66, 8-9 (2013)
Sociology Mathematics Physics Computer Science