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Relevance 8/10Data and MetricsIntermediate6 min read
Precision and Recall for Labelers
Precision measures correctness of predicted labels; recall measures coverage of true labels.
Why it matters for annotators
Understanding metric trade-offs helps annotators optimize for project objectives.
Visual mental model
TP, FP, FN balance -> metric interpretation.
Examples (bad vs good)
Scenario: Real annotation scenario involving Precision and Recall for Labelers
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.