thank you for the pointer! I was not aware of this interesting paper. It was probably developed concurrently to ours. Though the presentation of ideas is different, I was happy to see that the overall perspective the authors advance is consistent with how we think of LLMs as measurement tools.

Note also that we advocate the use of open models that can be run locally - this is crucial for ensuring replicability.

Bart, we do no promise unbiased results... We provide evidence of high accuracy with respect to a benchmark based on human coding. As pointed out in another response, Egami et al's DSL approach can improve on our approach. The field is moving fast (our paper was written a year ago).

But clearly, one can (should) use the DSL technique on the position estimates produced by our approach since the high accuracy we obtain does not guarantee the absence of systematic bias wrt human coding.

Thanks Nicolai for the pointer! Indeed Egami et al.'s technique came out after we wrote the core of our piece.