Uses deviations from rarefaction curves to detect whether folktale collectors sought out new material (explorers) or revisited known types (exploiters).
Introduces coverage-based methods for fairly comparing cultural diversity across folktale collections of unequal sampling depth.
Applies unseen species models to VOC historical records to estimate the true number of sailors missing from the archive.
Shows how functional diversity measures based on semantic similarity generalise traditional type-token lexical diversity metrics.
Introduces functional attribute diversity — which accounts for similarity between items — and estimates how much goes undetected in incomplete cultural samples.
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.
How Good-Turing frequency estimation underpins the Chao1 biodiversity estimator — and what that means for counting unseen cultural artefacts.