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Relevance 7/10Quality and QAIntermediate6 min read
Guideline Drift Detection
Guideline drift detection identifies when annotator behavior diverges from current written policy.
Why it matters for annotators
Drift detection prevents silent quality decay in long-running projects.
Visual mental model
Current policy vs live labels -> drift alerts -> recalibration.
Examples (bad vs good)
Scenario: Real annotation scenario involving Guideline Drift Detection
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.