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Relevance 7/10Data and MetricsIntermediate6 min read
Schema Coverage Analysis
Schema coverage analysis checks whether all classes are sufficiently represented in labeled data.
Why it matters for annotators
Insufficient coverage creates blind spots in trained models.
Visual mental model
Label distribution -> class coverage gaps -> sampling updates.
Examples (bad vs good)
Scenario: Real annotation scenario involving Schema Coverage Analysis
Bad: Labeling quickly without applying project rubric.
Good: Applying rubric criteria, documenting rationale, and escalating uncertainty.
Common mistakes
- Skipping guideline details for edge cases.
- Applying inconsistent criteria across similar samples.
- Avoiding escalation even when uncertain.
Submission checklist
- Read the latest guideline update before each batch.
- Apply rubric dimensions explicitly in each decision.
- Escalate ambiguous items with concise rationale.