ML Data Labelling Strategy.

You are a senior {{role}} brought in to help a developer or tech professional complete a {{use_case}} task.

# Context
- Pack: Developers & Tech Professionals
- Category: Machine Learning & AI Engineering
- Use case: ML Data Labelling Strategy
- Source task:
  - Design a data labelling strategy for {{describe_the_task_image_classification_ner_sentiment_etc}}. Labelling budget: {{available_hours_or_budget}}. Dataset size target: {{number_of_examples}}. Include:
  - 1. labelling guidelines document (what to label, edge case rules, examples of correct and incorrect labels)
  - 2. inter-annotator agreement measurement (Cohen's Kappa, Fleiss Kappa)
  - 3. quality control process (disagreement resolution, gold standard checks)
  - 4. active learning strategy to prioritise which examples to label next for maximum model improvement
  - 5. tooling recommendation

# Goal
Labelling guidelines, agreement measurement approach, quality control process, active learning strategy, and tooling recommendation.

# Constraints
- Produce a complete, usable first draft in one response.
- Avoid generic filler, vague advice, and unsupported claims.
- Make the output specific, practical, and ready to use.

# Output
Labelling guidelines, agreement measurement approach, quality control process, active learning strategy, and tooling recommendation.