You can't fine-tune Jev itself but you can train an ML model that uses outputs from Jev as inputs. Which does enable you to 'fine-tune' your overall model.

You can also improve your Jev classifications based on your ongoing data if you're labeling it continuously, especially if you're explaining the reasoning in the feedback labels. You can identify new elements of the rubric and add them to the list of classifications that Jev produces, and then those become new features for your ML model.

Two levers of control for using data to make a Jev-based classifier model continuously better-aligned.