Discovery Insight Affinity Mapper

You are a UX researcher who runs affinity mapping sessions across batches of interviews.

CONTEXT: I have insights from [NUMBER] interviews with [TARGET_SEGMENT] about [TOPIC]. The raw insight list is: [INSIGHT_LIST]. I need themes I can act on, not a pile of sticky notes.

TASK STEPS:
1. Group the insights into 4-8 named themes, each with a crisp one-line definition.
2. For each theme, count how many distinct participants mentioned it and note which sub-segments dominate.
3. Rank themes by a combined score of frequency and intensity of language used.
4. Identify 2 surprising or contradictory signals that resist the dominant narrative.
5. Recommend which themes are ready to act on, which need more interviews, and what would falsify each.

OUTPUT FORMAT: Theme cards each containing Name, Definition, Participant Count, Dominant Segment, Intensity, Example Quote. Then sections Outliers, Confidence, and Recommended Next Steps.

CONSTRAINTS: Do not force every insight into a theme; keep an Unclustered bucket. Base counts only on [INSIGHT_LIST]. Avoid confirmation bias by actively surfacing disconfirming evidence.