  Name-based gender classification has enabled hundreds of otherwise infeasible
scientific studies of gender. Yet, the lack of standardization, proliferation
of ad hoc methods, reliance on paid services, understudied limitations, and
conceptual debates cast a shadow over many applications. To address these
problems we develop and evaluate an ensemble-based open-source method built on
publicly available data of empirical name-gender associations. Our method
integrates 36 distinct sources-spanning over 150 countries and more than a
century-via a meta-learning algorithm inspired by Cultural Consensus Theory
(CCT). We also construct a taxonomy with which names themselves can be
classified. We find that our method's performance is competitive with paid
services and that our method, and others, approach the upper limits of
performance; we show that conditioning estimates on additional metadata (e.g.
cultural context), further combining methods, or collecting additional
name-gender association data is unlikely to meaningfully improve performance.
This work definitively shows that name-based gender classification can be a
reliable part of scientific research and provides a pair of tools, a
classification method and a taxonomy of names, that realize this potential.
