  Large language models have seen extraordinary growth in popularity due to
their human-like content generation capabilities. We show that these models can
also be used to successfully cluster human-generated content, with success
defined through the measures of distinctiveness and interpretability. This
success is validated by both human reviewers and ChatGPT, providing an
automated means to close the 'validation gap' that has challenged short-text
clustering. Comparing the machine and human approaches we identify the biases
inherent in each, and question the reliance on human-coding as the 'gold
standard'. We apply our methodology to Twitter bios and find characteristic
ways humans describe themselves, agreeing well with prior specialist work, but
with interesting differences characteristic of the medium used to express
identity.
