Shows how functional diversity measures based on semantic similarity generalise traditional type-token lexical diversity metrics.
Extends unseen species models to estimate the shared diversity between two cultural collections — how many artefacts they have in common that neither has observed.
Reframes population size estimation as a Bayesian regression problem to correct for heterogeneous detection probabilities in cultural samples.
Explores Zelterman's robust alternative to Chao1 — an estimator less sensitive to heterogeneity violations, tested on cultural and literary data.
Examines how standard unseen species estimators break down under sampling without replacement, and how to correct for it.
How Good-Turing frequency estimation underpins the Chao1 biodiversity estimator — and what that means for counting unseen cultural artefacts.