[SO-101] Dataset labeling consistency for classroom demos for educators schools (intermediate)
SO-101 discussion on dataset labeling consistency, class definitions, demo review, and practical QA for classroom robot-learning datasets.
A hidden problem in SO-101 classroom datasets is not only capture quality but labeling consistency: different people use the same label words in slightly different ways, which hurts training and evaluation later.
How are you keeping dataset labels consistent across many classroom demos or student teams?
Please share how you define labels, review edge cases, and catch inconsistency before a dataset becomes too messy to compare across sessions.
If you reply, include one exact labeling disagreement and one exact rule or review step that made labels more consistent.
This is especially valuable if you explain how a simple rubric improved label quality across multiple annotators.
If your class uses examples of good and bad labels, mention that. Searchers often need concrete examples more than process diagrams.
A small review checklist can be more useful here than a perfect taxonomy.