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Many research domains use data elicited from ‘citizen scientists’ when a direct measure of a process is expensive or infeasible. However, participants may report incorrect estimates or classifications due to their lack of skill. We demonstrate how Bayesian hierarchical models can be used to learn about latent variables of interest, while accounting for the participants’ abilities. The model is described...
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