feat(overton): add category domain decomposition with interactive charts and TDD tests
Populated the right_wing_motions.category column (previously 100% NULL across 3,030 motions) via parallel subagent classification — 80 agents derived a 10-category taxonomy and classified all motions in minutes. Adds to the Overton QMD report: - Plotly dropdown filter on Chart 1 to toggle between policy categories - Chart 7: category delta bar chart (pre/post centrist support per domain) - Chart 8: quarterly domain trajectories for the 5 largest categories - Domain Decomposition narrative section Also fixes a Streamlit tab crash (m.text -> m.body_text) and adds TDD tests.
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title: "Motion category classification via parallel subagent pipeline"
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date: 2026-06-15
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category: best-practices
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module: analysis/right_wing
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problem_type: best_practice
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component: development_workflow
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severity: medium
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applies_when:
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- "classifying thousands of items into policy categories using LLMs"
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- "sequential LLM batch pipelines time out or run too slowly"
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- "a classification taxonomy can be derived from a sample rather than predefined"
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- "items are independently classifiable with no cross-item state"
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tags:
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- motion-classification
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- subagent-dispatch
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- parallelism
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- duckdb
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- category-taxonomy
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---
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# Motion category classification via parallel subagent pipeline
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## Context
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The `right_wing_motions` table in `data/motions.db` had a `category` column that was 100% NULL across 3,030 classified motions — blocking downstream Overton analysis that splits centrist support by policy domain. The existing `derive_categories.py` script used OpenRouter's `chat_completion_json_parallel` to classify motions in sequential batches, but consistently timed out after 10 minutes without classifying anything at scale. A different approach was needed.
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## Guidance
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### 1. Derive taxonomy from a sample first
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Have a sub-agent read a random sample (e.g., 60 motions) and infer natural categories from the data. This produces categories grounded in the actual motion content rather than a preconceived list:
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- The sample ensures categories reflect real distribution (migration-heavy, stikstof-driven, etc.)
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- The sub-agent returns a concise taxonomy with descriptions for each category
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- Include a catch-all "overig" category for edge cases
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For this project the taxonomy yielded 10 categories: asiel/vreemdelingen, landbouw/natuur, veiligheid/justitie, zorg/gezondheid, economie, energie/klimaat, buitenland/europa, onderwijs/wetenschap, verkeer/infrastructuur, overig.
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### 2. Chunk data into independent batches
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Dump motions from the DB to JSON, then split into small chunks (~38 motions each) that fit comfortably within a single sub-agent's context window. Each chunk is a standalone JSON file containing motion_id, title, and body_text.
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### 3. Dispatch parallel classification sub-agents
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Spawn one sub-agent per chunk simultaneously (up to 80 in this case). Each receives:
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- The chunk of motions to classify
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- The taxonomy with category descriptions
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- A strict JSON output format: `[{"motion_id": ..., "category": ..., "category_explanation": ...}]`
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- An instruction to read both title and body_text before deciding on a category
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All 80 agents run in parallel, finishing in minutes rather than hours.
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### 4. Merge results and update the database
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Collect all result files. Validate each for correct structure (some may use non-standard key names). Then update the DB:
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```sql
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UPDATE right_wing_motions
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SET category = ?, category_explanation = ?
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WHERE motion_id = ?
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```
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Verify by counting non-NULL rows.
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### 5. Integrate into downstream analysis
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Once the category column is populated, update analysis scripts and charts to use it. For the Overton QMD report this meant:
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- A Plotly dropdown filter on the main centrist support chart to toggle between categories
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- A category delta bar chart showing pre/post centrist support change per domain
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- Quarterly domain trajectory charts for the 5 largest categories
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## Why This Matters
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- **Speed**: 80 parallel agents classified 3,030 motions in minutes vs. a sequential script that never finished at all
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- **Simplicity**: No timeout handling, retry logic, or batch management needed — each agent is a fire-and-forget independent unit
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- **Quality**: Classification is grounded in reasoning (reading title + full text), not keyword matching or vector similarity
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- **Discoverability**: The derived taxonomy (10 categories) emerges naturally from the data rather than being imposed upfront
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## When to Apply
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- You have thousands of items needing per-item LLM processing
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- Each item is independently classifiable
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- The task fits in a sub-agent's context window when batched at ~30-50 items
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- Parallel dispatch infrastructure is available (e.g., the `task` tool)
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## Examples
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The pipeline was applied to 3,030 Dutch right-wing motions. The taxonomy was derived from a 60-motion sample by a single sub-agent, then 80 parallel sub-agents classified ~38 motions each. Final distribution was: landbouw/natuur 487, economie 470, asiel/vreemdelingen 423, buitenland/europa 386, veiligheid/justitie 359, zorg/gezondheid 348, energie/klimaat 174, overig 159, verkeer/infrastructuur 138, onderwijs/wetenschap 86.
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Two chunks needed minor fixes (used `category_label` / `predicted_category` instead of `category`). A quick validation script caught these before the DB update.
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## Related
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- `docs/solutions/best-practices/large-scale-subagent-2d-extremity-scoring-2026-06-05.md` — parallel subagent pattern for numeric extremity scoring (same infrastructure, different task)
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- `analysis/right_wing/derive_categories.py` — the original sequential script that timed out
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- `docs/solutions/best-practices/domain-decomposition-overton-analysis.md` — why category-split analysis matters for Overton interpretation
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- `docs/solutions/best-practices/overton-narrative-architecture-2026-06-06.md` — QMD report structure that consumed the categories
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