For textcat the model is designed so that the predictions for all categories always add up to 1.0, so if you just have one category, it will predict 1.0 for that category every single text.
For textcat_multilabel each individual category prediction can range from 0.0-1.0 independently from the predictions for the other categories for that text.
It's possible the default settings/options in prodigy could be improved for this particular kind of task, though. What are the commands you're using in your workflow?