We used a linear mixed effects model to predict the Likert scale ratings for each of our 14 qualitative dimensions. We used poem authorship (human or AI), framing condition (told human, told AI, or told nothing), and their interaction as fixed effects. As specified in our preregistration, we initially planned to include four random effects: random intercepts per participant, random slope of poem authorship per participant, random intercept per poem, and random slope of framing condition per poem. As in Study 1, we followed19 in checking the models for overparameterization; PCA dimensionality reduction revealed that the models were overparameterized, specifically because of the random slopes for framing condition per poem. An attempt to fit a zero-correlation-parameter model did not prevent overparameterization; we therefore fit a reduced model for each DV without the random slopes for framing condition.