diff --git a/analysis/tabs/overton.py b/analysis/tabs/overton.py
index bcb92b4..8d6ec03 100644
--- a/analysis/tabs/overton.py
+++ b/analysis/tabs/overton.py
@@ -182,7 +182,7 @@ def _render_motion_browser(con: duckdb.DuckDBPyConnection) -> None:
     st.subheader("Right-Wing Motions Browser")
 
     df = con.execute("""
-        SELECT r.year, r.title, m.text, r.centrist_support_strict, r.category
+        SELECT r.year, r.title, m.body_text, r.centrist_support_strict, r.category
         FROM right_wing_motions r
         LEFT JOIN motions m ON r.motion_id = m.id
         WHERE r.classified = TRUE
diff --git a/docs/plans/2026-06-15-001-feat-category-domain-decomposition-plan.md b/docs/plans/2026-06-15-001-feat-category-domain-decomposition-plan.md
new file mode 100644
index 0000000..367e18d
--- /dev/null
+++ b/docs/plans/2026-06-15-001-feat-category-domain-decomposition-plan.md
@@ -0,0 +1,167 @@
+---
+title: "feat: Category domain decomposition for Overton report"
+type: feat
+status: active
+date: 2026-06-15
+---
+
+# feat: Category domain decomposition for Overton report
+
+## Summary
+
+Add 3 new Plotly charts and a narrative section to `reports/overton_window/overton_window.qmd` that decompose the Overton shift by policy category (asiel/vreemdelingen, landbouw/natuur, energie/klimaat, etc.), making visible which domains drove the shift, which resisted it, and how each category's centrist support evolved over time. The yearly CS chart gains category filtering via Plotly dropdown menu.
+
+---
+
+## Problem Frame
+
+The current Overton report treats all right-wing motions as a single aggregate. The synthesis mentions migration vs non-migration, but with 10 categories now populated across all 3,030 motions, we can show a richer picture: energie/klimaat had the second-largest CS surge (+0.107), landbouw/natuur actually declined (−0.063), and onderwijs/wetenschap barely moved (+0.053) despite being the highest-consensus domain. These stories are invisible in the current charts.
+
+---
+
+## Requirements
+
+- R1. Add a horizontal bar chart showing pre/post centrist support delta per category, sorted by magnitude
+- R2. Add category filtering to the yearly CS timeline (Chart 1) via Plotly dropdown menu, so users can view a single category or "All right-wing"
+- R3. Add domain trajectories to the quarterly chart showing 4-5 key categories as separate lines
+- R4. Add a "Domain Decomposition" narrative section with the category delta table and interpretive prose
+- R5. All new charts must use the existing Plotly styling conventions (colors, template, height)
+
+---
+
+## Scope Boundaries
+
+- No changes to the synthesis markdown or other analysis scripts
+- No changes to the DB or data pipeline
+- No interactive filtering beyond Plotly's built-in updatemenu (no Streamlit/JS)
+
+---
+
+## Key Technical Decisions
+
+- **Plotly updatemenu for filtering:** Uses Plotly's built-in `updatemenus` with `buttons` to toggle between "All right-wing" (current line) and individual categories. No external JS or Dash needed.
+- **Color scheme for categories:** Use a qualitative 10-color palette (Plotly `alphabet` or `set2`), not PARTY_COLOURS, to avoid confusion with party lines.
+- **New chart cells inserted after existing Chart 1:** Category delta bar chart goes after the yearly CS chart (Chart 1) and its narrative. Domain trajectories go in the quarterly section (Chart 6). A new "Domain Decomposition" section links them.
+
+---
+
+## Open Questions
+
+### Resolved During Planning
+
+- **Standalone HTML filtering:** Plotly updatemenu works in standalone HTML (confirmed via existing usage in the repo — the Overton report is a standalone HTML file with embedded Plotly).
+
+### Deferred to Implementation
+
+- **Whether to show all 10 categories or a curated subset:** The quarterly trajectories chart should show at most 5-6 lines to avoid visual clutter. The delta bar chart can show all 10.
+- **Exact Plotly color assignment to each category:** Will match categories to a qualitative palette at implementation time.
+
+---
+
+## Implementation Units
+
+- U1. **[Add category delta bar chart and dropdown-filtered yearly CS]**
+
+**Goal:** Two linked chart additions: (1) a category delta horizontal bar chart, (2) convert Chart 1's yearly CS line chart to support category filtering via Plotly updatemenu.
+
+**Requirements:** R1, R2, R5
+
+**Dependencies:** None
+
+**Files:**
+- Modify: `reports/overton_window/overton_window.qmd`
+
+**Approach:**
+- Compute yearly CS per category via SQL: `SELECT year, category, AVG(centrist_support_strict) as cs, COUNT(*) as n FROM right_wing_motions WHERE classified = TRUE GROUP BY year, category`
+- For the delta chart (new cell): compute pre/post CS per category, then use a horizontal bar (go.Bar with orientation='h'), sorted by delta descending. Color bars: green for positive delta, red for negative.
+- For Chart 1 modification: extend the existing yearly SQL to GROUP BY year, category. In Python, pivot to get per-category columns. Create a go.Figure with all categories as traces plus an aggregate "All right-wing" trace. Add an updatemenu dropdown with buttons: "All right-wing" (restores all visible with only the aggregate line shown) and each category name (shows only that category's trace).
+- Keep the existing pre/post mean lines and the break-year vertical line intact. The dropdown only controls which category trace is visible.
+- Use a 10-color qualitative palette from plotly.express.colors.qualitative.
+
+**Patterns to follow:**
+- Existing Chart 1 for SQL pattern, figure layout, annotation style
+- Existing Chart 6 for quarterly trajectory styling
+
+**Test scenarios:**
+- N/A — this is a Quarto document rendering change. Verify by rendering the QMD and checking that (a) the delta chart shows all 10 categories, (b) the dropdown in Chart 1 cycles through categories correctly, (c) pre/post mean lines remain visible in all views.
+
+**Verification:**
+- `uv run quarto render reports/overton_window/overton_window.qmd` succeeds
+- `overton_report.html` contains the new delta chart section and Chart 1 responds to dropdown interaction
+
+---
+
+- U2. **[Add domain trajectories to quarterly chart]**
+
+**Goal:** Enhance the existing quarterly chart (Chart 6) by overlaying 4-5 key category lines alongside the aggregate.
+
+**Requirements:** R3, R5
+
+**Dependencies:** U1 (the SQL for per-category yearly CS shares the same data approach; the quarterly chart needs its own SQL)
+
+**Files:**
+- Modify: `reports/overton_window/overton_window.qmd`
+
+**Approach:**
+- Select 5 categories to show as individual lines: asiel/vreemdelingen, energie/klimaat, buitenland/europa, landbouw/natuur, economie. These are the categories with largest deltas, largest volumes, or most story value.
+- Compute quarterly CS per category: `SELECT EXTRACT(YEAR FROM m.date) AS y, CEIL(EXTRACT(MONTH FROM m.date) / 3.0) AS q, r.category, AVG(r.centrist_support_strict) AS cs, COUNT(*) AS n FROM right_wing_motions r JOIN motions m ON r.motion_id = m.id WHERE r.classified = TRUE AND m.date IS NOT NULL AND r.category IN (...) GROUP BY y, q, r.category`
+- Add each category as a separate go.Scatter trace with distinct colors and dashed lines (to distinguish from the aggregate solid line).
+- Keep the existing aggregate line in solid bold. Use the same inflection/peak annotations.
+- Add a legend entry for each category.
+
+**Patterns to follow:**
+- Existing Chart 6 for quarterly SQL, figure layout, inflection/peak annotations
+
+**Test scenarios:**
+- N/A — verify by rendering and checking that 5 category traces appear with distinct colors and dashed styles alongside the aggregate.
+
+**Verification:**
+- `uv run quarto render reports/overton_window/overton_window.qmd` succeeds
+- The quarterly chart shows 5 category lines with a legend
+
+---
+
+- U3. **[Add Domain Decomposition narrative section]**
+
+**Goal:** Add a heading-2 section "Domain Decomposition" between Indicator 1 and Indicator 2, with a markdown table of category deltas and 2-3 paragraphs of interpretive prose.
+
+**Requirements:** R4
+
+**Dependencies:** U1 (the delta data is used; the table can be hardcoded from the pre-computed values or computed via inline SQL)
+
+**Files:**
+- Modify: `reports/overton_window/overton_window.qmd`
+
+**Approach:**
+- Insert the section after the existing Indicator 1 narrative text and before the "## Indicator 2: Spatial Divergence" heading.
+- Include a markdown table with columns: Category, Pre-2024 CS, Post-2024 CS, Delta, Volume, Extremity gap (M−S). Use the known data from the DB queries.
+- Write 3 paragraphs:
+  1. Overview: which categories drove the shift (migration, energy, foreign affairs) and which resisted (agriculture, healthcare, infrastructure).
+  2. The polarization paradox: landbouw/natuur and zorg/gezondheid as domains where centrist support declined despite content moderation.
+  3. The consensus domains: onderwijs/wetenschap and economie as stable high-CS categories where the window didn't need to shift.
+- Reference the category delta chart (U1) and domain trajectories chart (U2) by their cell labels.
+
+**Patterns to follow:**
+- Existing prose style in Indicator 1 and Indicator 2 sections
+
+**Test scenarios:**
+- N/A — prose section. Review for factual accuracy against the DB data.
+
+**Verification:**
+- Section renders in the HTML output with correct numbers and coherent prose
+
+---
+
+## System-Wide Impact
+
+- **Interaction graph:** The QMD is the only file. No analysis scripts, DB schemas, or other artifacts are affected.
+- **Unchanged invariants:** All existing charts, narrative sections, and data remain intact. New cells are added after existing ones, and the new section is inserted between existing sections.
+
+---
+
+## Risks & Dependencies
+
+| Risk | Mitigation |
+|------|------------|
+| Plotly updatemenu may not fully render in Quarto HTML output | Test with a minimal prototype in an isolated QMD cell first; fallback is a faceted chart showing all categories as subplots |
+| 5 category lines on the quarterly chart may be visually noisy | Use dashed lines for category traces and a solid bold line for aggregate. If too noisy, reduce to 3 categories |
diff --git a/docs/solutions/best-practices/motion-category-classification-subagent-pipeline-2026-06-15.md b/docs/solutions/best-practices/motion-category-classification-subagent-pipeline-2026-06-15.md
new file mode 100644
index 0000000..3781676
--- /dev/null
+++ b/docs/solutions/best-practices/motion-category-classification-subagent-pipeline-2026-06-15.md
@@ -0,0 +1,98 @@
+---
+title: "Motion category classification via parallel subagent pipeline"
+date: 2026-06-15
+category: best-practices
+module: analysis/right_wing
+problem_type: best_practice
+component: development_workflow
+severity: medium
+applies_when:
+  - "classifying thousands of items into policy categories using LLMs"
+  - "sequential LLM batch pipelines time out or run too slowly"
+  - "a classification taxonomy can be derived from a sample rather than predefined"
+  - "items are independently classifiable with no cross-item state"
+tags:
+  - motion-classification
+  - subagent-dispatch
+  - parallelism
+  - duckdb
+  - category-taxonomy
+---
+
+# Motion category classification via parallel subagent pipeline
+
+## Context
+
+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.
+
+## Guidance
+
+### 1. Derive taxonomy from a sample first
+
+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:
+
+- The sample ensures categories reflect real distribution (migration-heavy, stikstof-driven, etc.)
+- The sub-agent returns a concise taxonomy with descriptions for each category
+- Include a catch-all "overig" category for edge cases
+
+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.
+
+### 2. Chunk data into independent batches
+
+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.
+
+### 3. Dispatch parallel classification sub-agents
+
+Spawn one sub-agent per chunk simultaneously (up to 80 in this case). Each receives:
+- The chunk of motions to classify
+- The taxonomy with category descriptions
+- A strict JSON output format: `[{"motion_id": ..., "category": ..., "category_explanation": ...}]`
+- An instruction to read both title and body_text before deciding on a category
+
+All 80 agents run in parallel, finishing in minutes rather than hours.
+
+### 4. Merge results and update the database
+
+Collect all result files. Validate each for correct structure (some may use non-standard key names). Then update the DB:
+
+```sql
+UPDATE right_wing_motions
+SET category = ?, category_explanation = ?
+WHERE motion_id = ?
+```
+
+Verify by counting non-NULL rows.
+
+### 5. Integrate into downstream analysis
+
+Once the category column is populated, update analysis scripts and charts to use it. For the Overton QMD report this meant:
+- A Plotly dropdown filter on the main centrist support chart to toggle between categories
+- A category delta bar chart showing pre/post centrist support change per domain
+- Quarterly domain trajectory charts for the 5 largest categories
+
+## Why This Matters
+
+- **Speed**: 80 parallel agents classified 3,030 motions in minutes vs. a sequential script that never finished at all
+- **Simplicity**: No timeout handling, retry logic, or batch management needed — each agent is a fire-and-forget independent unit
+- **Quality**: Classification is grounded in reasoning (reading title + full text), not keyword matching or vector similarity
+- **Discoverability**: The derived taxonomy (10 categories) emerges naturally from the data rather than being imposed upfront
+
+## When to Apply
+
+- You have thousands of items needing per-item LLM processing
+- Each item is independently classifiable
+- The task fits in a sub-agent's context window when batched at ~30-50 items
+- Parallel dispatch infrastructure is available (e.g., the `task` tool)
+
+## Examples
+
+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.
+
+Two chunks needed minor fixes (used `category_label` / `predicted_category` instead of `category`). A quick validation script caught these before the DB update.
+
+## Related
+
+- `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)
+- `analysis/right_wing/derive_categories.py` — the original sequential script that timed out
+- `docs/solutions/best-practices/domain-decomposition-overton-analysis.md` — why category-split analysis matters for Overton interpretation
+- `docs/solutions/best-practices/overton-narrative-architecture-2026-06-06.md` — QMD report structure that consumed the categories
diff --git a/reports/overton_window/STATUS.md b/reports/overton_window/STATUS.md
index a67ebf7..f6b8822 100644
--- a/reports/overton_window/STATUS.md
+++ b/reports/overton_window/STATUS.md
@@ -108,7 +108,7 @@ Three tiers:
 - [ ] Mechanism taxonomy revision (κ=0.41 → improve agreement)
 - [ ] Forward-looking scenario analysis (permanent vs temporary shift)
 - [ ] Anti-institutional pivot deep-dive (abolition → contestation)
-- [ ] Re-populate category column in right_wing_motions (wiped by DROP TABLE)
+- [x] Re-populate category column in right_wing_motions (wiped by DROP TABLE)
 
 ### Presentation
 - [ ] Quarto blog post with interactive charts
diff --git a/reports/overton_window/breakpoint_analysis.md b/reports/overton_window/breakpoint_analysis.md
index 4df24a2..da9b35e 100644
--- a/reports/overton_window/breakpoint_analysis.md
+++ b/reports/overton_window/breakpoint_analysis.md
@@ -1,7 +1,5 @@
 # Overton Window Breakpoint Analysis (2D Extremity)
 
-> **Part of the Overton Window Analysis.** See the [synthesis report](overton_window_synthesis.md) for the integrated narrative, or the [interactive article](overton_window.qmd) for the full story with charts.
-
 **Goal:** Quantify the 2024 structural break in centrist support
 and content extremity for right-wing motions in the Tweede Kamer.
 
@@ -73,8 +71,8 @@ Migration = category `asiel/vreemdelingen`. Non-migration = all other categories
 
 | Domain | Pre-2024 Mean CS | Post-2024 Mean CS | Δ CS |
 |--------|-----------------|------------------|------|
-| Migration | nan | nan | +nan |
-| Non-migration | 0.425 | 0.482 | +0.057 |
+| Migration | 0.125 | 0.343 | +0.218 |
+| Non-migration | 0.436 | 0.509 | +0.073 |
 
 ## 5. Material Impact-Stratified Centrist Support
 
@@ -161,26 +159,26 @@ If both rose equally, a systemic factor (coalition change, polarization) is at w
 
 | # | Year | Category | Stijl | Materieel | Bucket | Agreed? | Driver |
 |---|------|----------|-------|-----------|--------|---------|--------|
-| 1 | 2021 | other | 1 | 1 | 1-2 (mild) | | |
-| 2 | 2020 | other | 1 | 1 | 1-2 (mild) | | |
-| 3 | 2023 | other | 1 | 1 | 1-2 (mild) | | |
-| 4 | 2023 | other | 3 | 1 | 1-2 (mild) | | |
-| 5 | 2022 | other | 1 | 1 | 1-2 (mild) | | |
-| 6 | 2021 | other | 1 | 2 | 2-3 (moderate) | | |
-| 7 | 2020 | other | 1 | 2 | 2-3 (moderate) | | |
-| 8 | 2020 | other | 2 | 2 | 2-3 (moderate) | | |
-| 9 | 2025 | other | 1 | 2 | 2-3 (moderate) | | |
-| 10 | 2019 | other | 2 | 2 | 2-3 (moderate) | | |
-| 11 | 2019 | other | 3 | 3 | 3-4 (high) | | |
-| 12 | 2020 | other | 3 | 3 | 3-4 (high) | | |
-| 13 | 2020 | other | 2 | 3 | 3-4 (high) | | |
-| 14 | 2020 | other | 4 | 3 | 3-4 (high) | | |
-| 15 | 2022 | other | 2 | 3 | 3-4 (high) | | |
-| 16 | 2025 | other | 2 | 4 | 4-5 (extreme) | | |
-| 17 | 2021 | other | 2 | 4 | 4-5 (extreme) | | |
-| 18 | 2025 | other | 3 | 4 | 4-5 (extreme) | | |
-| 19 | 2023 | other | 2 | 4 | 4-5 (extreme) | | |
-| 20 | 2019 | other | 2 | 5 | 4-5 (extreme) | | |
+| 1 | 2021 | landbouw/natuur | 1 | 1 | 1-2 (mild) | | |
+| 2 | 2020 | veiligheid/justitie | 1 | 1 | 1-2 (mild) | | |
+| 3 | 2023 | veiligheid/justitie | 1 | 1 | 1-2 (mild) | | |
+| 4 | 2023 | overig | 3 | 1 | 1-2 (mild) | | |
+| 5 | 2022 | verkeer/infrastructuur | 1 | 1 | 1-2 (mild) | | |
+| 6 | 2021 | landbouw/natuur | 1 | 2 | 2-3 (moderate) | | |
+| 7 | 2020 | veiligheid/justitie | 1 | 2 | 2-3 (moderate) | | |
+| 8 | 2020 | buitenland/europa | 2 | 2 | 2-3 (moderate) | | |
+| 9 | 2025 | energie/klimaat | 1 | 2 | 2-3 (moderate) | | |
+| 10 | 2019 | buitenland/europa | 2 | 2 | 2-3 (moderate) | | |
+| 11 | 2019 | overig | 3 | 3 | 3-4 (high) | | |
+| 12 | 2020 | zorg/gezondheid | 3 | 3 | 3-4 (high) | | |
+| 13 | 2020 | buitenland/europa | 2 | 3 | 3-4 (high) | | |
+| 14 | 2020 | verkeer/infrastructuur | 4 | 3 | 3-4 (high) | | |
+| 15 | 2022 | energie/klimaat | 2 | 3 | 3-4 (high) | | |
+| 16 | 2025 | asiel/vreemdelingen | 2 | 4 | 4-5 (extreme) | | |
+| 17 | 2021 | zorg/gezondheid | 2 | 4 | 4-5 (extreme) | | |
+| 18 | 2025 | asiel/vreemdelingen | 3 | 4 | 4-5 (extreme) | | |
+| 19 | 2023 | economie | 2 | 4 | 4-5 (extreme) | | |
+| 20 | 2019 | veiligheid/justitie | 2 | 5 | 4-5 (extreme) | | |
 
 
 ## 10. Limitations
diff --git a/reports/overton_window/breakpoint_figure_1.png b/reports/overton_window/breakpoint_figure_1.png
index 5516cd5..5135db1 100644
Binary files a/reports/overton_window/breakpoint_figure_1.png and b/reports/overton_window/breakpoint_figure_1.png differ
diff --git a/reports/overton_window/breakpoint_figure_2.png b/reports/overton_window/breakpoint_figure_2.png
index 39604e1..b030b8b 100644
Binary files a/reports/overton_window/breakpoint_figure_2.png and b/reports/overton_window/breakpoint_figure_2.png differ
diff --git a/reports/overton_window/causal_timing.md b/reports/overton_window/causal_timing.md
index 7181dfe..ec3ba69 100644
--- a/reports/overton_window/causal_timing.md
+++ b/reports/overton_window/causal_timing.md
@@ -1,7 +1,5 @@
 # Causal Timing: Centrist Support Shift for Right-Wing Motions
 
-> **Part of the Overton Window Analysis.** See the [synthesis report](overton_window_synthesis.md) for the integrated narrative, or the [interactive article](overton_window.qmd) for the full story with charts.
-
 **Goal:** Identify the exact timing of the centrist support shift and correlate it with
 political events to distinguish between competing causal explanations.
 
diff --git a/reports/overton_window/extremity_2d_temporal.md b/reports/overton_window/extremity_2d_temporal.md
index ea4d4bc..7023c02 100644
--- a/reports/overton_window/extremity_2d_temporal.md
+++ b/reports/overton_window/extremity_2d_temporal.md
@@ -1,7 +1,5 @@
 # 2D Extremity Temporal Decomposition
 
-> **Part of the Overton Window Analysis.** See the [synthesis report](overton_window_synthesis.md) for the integrated narrative, or the [interactive article](overton_window.qmd) for the full story with charts.
-
 **Goal:** Test whether the "flat single-dimension trend" masks diverging trajectories
 when stylistic and material extremity scores are analyzed separately over time.
 
@@ -17,8 +15,8 @@ when stylistic and material extremity scores are analyzed separately over time.
 ## 1. Key Findings
 
 **Overall correlation r(stijl, materieel):** 0.472 (p=0.000000)
-**Migration domain r(stijl, materieel):** N/A (p=N/A, n=0)
-**Non-migration domain r(stijl, materieel):** 0.472 (p=0.000000, n=3030)
+**Migration domain r(stijl, materieel):** 0.471 (p=0.000000, n=423)
+**Non-migration domain r(stijl, materieel):** 0.424 (p=0.000000, n=2607)
 
 ---
 
@@ -40,14 +38,14 @@ when stylistic and material extremity scores are analyzed separately over time.
 | 2016 * | 6 | 1.667 | 2.333 | 2.000 | 0.667 | 0 | N/A | N/A | 6 | 1.667 | 2.333 | N/A |
 | 2017 * | 0 | N/A | N/A | N/A | N/A | 0 | N/A | N/A | 0 | N/A | N/A | N/A |
 | 2018 * | 5 | 1.000 | 1.400 | 1.400 | 0.400 | 0 | N/A | N/A | 5 | 1.000 | 1.400 | N/A |
-| 2019 | 195 | 2.046 | 2.913 | 2.138 | 0.867 | 0 | N/A | N/A | 195 | 2.046 | 2.913 | 0.491 |
-| 2020 | 469 | 2.228 | 2.906 | 2.262 | 0.678 | 0 | N/A | N/A | 469 | 2.228 | 2.906 | 0.603 |
-| 2021 | 425 | 1.755 | 2.976 | 2.240 | 1.221 | 0 | N/A | N/A | 425 | 1.755 | 2.976 | 0.503 |
-| 2022 | 446 | 1.800 | 2.525 | 2.161 | 0.724 | 0 | N/A | N/A | 446 | 1.800 | 2.525 | 0.429 |
-| 2023 | 365 | 1.575 | 2.690 | 2.238 | 1.115 | 0 | N/A | N/A | 365 | 1.575 | 2.690 | 0.346 |
-| 2024 | 469 | 1.680 | 2.582 | 1.985 | 0.902 | 0 | N/A | N/A | 469 | 1.680 | 2.582 | 0.391 |
-| 2025 | 455 | 1.695 | 2.332 | 2.253 | 0.637 | 0 | N/A | N/A | 455 | 1.695 | 2.332 | 0.592 |
-| 2026 | 195 | 2.015 | 2.405 | 2.331 | 0.390 | 0 | N/A | N/A | 195 | 2.015 | 2.405 | 0.335 |
+| 2019 | 195 | 2.046 | 2.913 | 2.138 | 0.867 | 17 | 2.941 | 2.824 | 178 | 1.961 | 2.921 | 0.491 |
+| 2020 | 469 | 2.228 | 2.906 | 2.262 | 0.678 | 36 | 3.389 | 3.472 | 433 | 2.132 | 2.859 | 0.603 |
+| 2021 | 425 | 1.755 | 2.976 | 2.240 | 1.221 | 30 | 2.967 | 3.833 | 395 | 1.663 | 2.911 | 0.503 |
+| 2022 | 446 | 1.800 | 2.525 | 2.161 | 0.724 | 77 | 2.260 | 3.026 | 369 | 1.705 | 2.420 | 0.429 |
+| 2023 | 365 | 1.575 | 2.690 | 2.238 | 1.115 | 65 | 2.231 | 3.308 | 300 | 1.433 | 2.557 | 0.346 |
+| 2024 | 469 | 1.680 | 2.582 | 1.985 | 0.902 | 63 | 2.571 | 3.175 | 406 | 1.542 | 2.490 | 0.391 |
+| 2025 | 455 | 1.695 | 2.332 | 2.253 | 0.637 | 94 | 2.543 | 3.277 | 361 | 1.474 | 2.086 | 0.592 |
+| 2026 | 195 | 2.015 | 2.405 | 2.331 | 0.390 | 41 | 2.707 | 2.927 | 154 | 1.831 | 2.266 | 0.335 |
 
 > * Years with <50 scored motions; confidence intervals are wider or N/A.
 
@@ -80,21 +78,29 @@ and material extremity.
 | 2017 | N/A | N/A | 0 | All |
 | 2018 | N/A | N/A | 5 | All |
 | 2019 | 0.491 | 0.000000 | 195 | All |
-| | 0.491 | 0.000000 | 195 | Non-migration |
+| | 0.769 | 0.000313 | 17 | Migration |
+| | 0.500 | 0.000000 | 178 | Non-migration |
 | 2020 | 0.603 | 0.000000 | 469 | All |
-| | 0.603 | 0.000000 | 469 | Non-migration |
+| | 0.352 | 0.035486 | 36 | Migration |
+| | 0.604 | 0.000000 | 433 | Non-migration |
 | 2021 | 0.503 | 0.000000 | 425 | All |
-| | 0.503 | 0.000000 | 425 | Non-migration |
+| | 0.600 | 0.000460 | 30 | Migration |
+| | 0.455 | 0.000000 | 395 | Non-migration |
 | 2022 | 0.429 | 0.000000 | 446 | All |
-| | 0.429 | 0.000000 | 446 | Non-migration |
+| | 0.557 | 0.000000 | 77 | Migration |
+| | 0.327 | 0.000000 | 369 | Non-migration |
 | 2023 | 0.346 | 0.000000 | 365 | All |
-| | 0.346 | 0.000000 | 365 | Non-migration |
+| | 0.487 | 0.000039 | 65 | Migration |
+| | 0.218 | 0.000146 | 300 | Non-migration |
 | 2024 | 0.391 | 0.000000 | 469 | All |
-| | 0.391 | 0.000000 | 469 | Non-migration |
+| | 0.155 | 0.224438 | 63 | Migration |
+| | 0.355 | 0.000000 | 406 | Non-migration |
 | 2025 | 0.592 | 0.000000 | 455 | All |
-| | 0.592 | 0.000000 | 455 | Non-migration |
+| | 0.561 | 0.000000 | 94 | Migration |
+| | 0.378 | 0.000000 | 361 | Non-migration |
 | 2026 | 0.335 | 0.000002 | 195 | All |
-| | 0.335 | 0.000002 | 195 | Non-migration |
+| | 0.536 | 0.000308 | 41 | Migration |
+| | 0.098 | 0.227990 | 154 | Non-migration |
 
 ---
 
@@ -137,8 +143,8 @@ trend is an accurate summary (no masked divergence).
 
 | Domain | Pre Mean Stijl | Pre Mean Mat | Post Mean Stijl | Post Mean Mat | Pre Gap | Post Gap | Pre r | Post r |
 |--------|---------------|-------------|----------------|---------------|---------|----------|-------|--------|
-| Migration | N/A | N/A | N/A | N/A | N/A | N/A | N/A | N/A |
-| Non-migration | 1.725 | 2.535 | 1.797 | 2.440 | 0.810 | 0.643 | 0.475 | 0.439 |
+| Migration | 2.757 | 3.293 | 2.607 | 3.126 | 0.535 | 0.519 | 0.553 | 0.417 |
+| Non-migration | 1.651 | 2.486 | 1.616 | 2.281 | 0.835 | 0.665 | 0.421 | 0.277 |
 
 ---
 
diff --git a/reports/overton_window/extremity_2d_temporal_figure.png b/reports/overton_window/extremity_2d_temporal_figure.png
index 6016353..9957fd7 100644
Binary files a/reports/overton_window/extremity_2d_temporal_figure.png and b/reports/overton_window/extremity_2d_temporal_figure.png differ
diff --git a/reports/overton_window/left_wing_response.md b/reports/overton_window/left_wing_response.md
index c4d8bce..77aa0c2 100644
--- a/reports/overton_window/left_wing_response.md
+++ b/reports/overton_window/left_wing_response.md
@@ -1,7 +1,5 @@
 # Left-Wing Response to Right-Wing Motions
 
-> **Part of the Overton Window Analysis.** See the [synthesis report](overton_window_synthesis.md) for the integrated narrative, or the [interactive article](overton_window.qmd) for the full story with charts.
-
 **Goal:** Determine whether the centrist support surge reflects right-wing
 moderation, centrist acceptance, or left-wing opposition hardening.
 
@@ -83,10 +81,10 @@ Non-migration = all other categories.
 
 | Domain | Period | Left Support | Centrist Support | Gap | N |
 |--------|--------|-------------|-----------------|-----|---|
-| migration | Pre-2024 | N/A | N/A | N/A | 0 |
-| migration | Post-2024 | N/A | N/A | N/A | 0 |
-| non-migration | Pre-2024 | 0.2680 | 0.425 | +0.157 | 1911 |
-| non-migration | Post-2024 | 0.2044 | 0.482 | +0.277 | 1119 |
+| migration | Pre-2024 | 0.0465 | 0.125 | +0.079 | 225 |
+| migration | Post-2024 | 0.0887 | 0.343 | +0.255 | 198 |
+| non-migration | Pre-2024 | 0.2828 | 0.436 | +0.153 | 1686 |
+| non-migration | Post-2024 | 0.2291 | 0.509 | +0.280 | 921 |
 
 
 ---
diff --git a/reports/overton_window/left_wing_response_figure.png b/reports/overton_window/left_wing_response_figure.png
index ba28f26..6eae8fa 100644
Binary files a/reports/overton_window/left_wing_response_figure.png and b/reports/overton_window/left_wing_response_figure.png differ
diff --git a/reports/overton_window/mechanism_classification.md b/reports/overton_window/mechanism_classification.md
index ceeda9d..6aade67 100644
--- a/reports/overton_window/mechanism_classification.md
+++ b/reports/overton_window/mechanism_classification.md
@@ -1,7 +1,5 @@
 # Mechanism Classification Report
 
-> **Part of the Overton Window Analysis.** See the [synthesis report](overton_window_synthesis.md) for the integrated narrative, or the [interactive article](overton_window.qmd) for the full story with charts.
-
 **Sample:** 200 motions (stratified: 50 pre-2024, 150 post-2024)
 **Classified:** 200 motions | **Unclassified:** 0
 
diff --git a/reports/overton_window/overton_window.qmd b/reports/overton_window/overton_window.qmd
index 878ffbd..6171407 100644
--- a/reports/overton_window/overton_window.qmd
+++ b/reports/overton_window/overton_window.qmd
@@ -117,7 +117,7 @@ party-level axis scores within each bloc.
 #| fig-cap: "Centrist Support for Right-Wing Motions Over Time (2016–2026)"
 #| column: page
 
-yearly = con.execute("""
+yearly_agg = con.execute("""
     SELECT
         year,
         AVG(centrist_support_strict) AS mean_cs,
@@ -128,44 +128,102 @@ yearly = con.execute("""
     GROUP BY year ORDER BY year
 """).fetchdf()
 
+yearly_cat = con.execute("""
+    SELECT
+        year,
+        category,
+        AVG(centrist_support_strict) AS cs,
+        COUNT(*) AS n
+    FROM right_wing_motions
+    WHERE classified = TRUE AND year >= 2017 AND category IS NOT NULL
+    GROUP BY year, category
+    ORDER BY year, category
+""").fetchdf()
+
+categories_list = sorted(yearly_cat["category"].unique())
+cat_colors = ["#66C2A5", "#FC8D62", "#8DA0CB", "#E78AC3", "#A6D854",
+              "#FFD92F", "#E5C494", "#B3B3B3", "#1E88E5", "#D81B60"]
+cat_labels = {
+    "asiel/vreemdelingen": "Asiel & Vreemdelingen",
+    "landbouw/natuur": "Landbouw & Natuur",
+    "veiligheid/justitie": "Veiligheid & Justitie",
+    "zorg/gezondheid": "Zorg & Gezondheid",
+    "economie": "Economie",
+    "energie/klimaat": "Energie & Klimaat",
+    "buitenland/europa": "Buitenland & Europa",
+    "onderwijs/wetenschap": "Onderwijs & Wetenschap",
+    "verkeer/infrastructuur": "Verkeer & Infrastructuur",
+    "overig": "Overig",
+}
+
 fig1 = go.Figure()
 
 fig1.add_trace(go.Scatter(
-    x=yearly["year"], y=yearly["mean_cs"],
+    x=yearly_agg["year"], y=yearly_agg["mean_cs"],
     mode="lines+markers", name="All right-wing",
     line=dict(color="#002366", width=3),
     marker=dict(size=8),
     error_y=dict(
         type="data",
-        array=1.96 * yearly["std_cs"] / np.sqrt(yearly["n"]),
+        array=1.96 * yearly_agg["std_cs"] / np.sqrt(yearly_agg["n"]),
         visible=True, thickness=0.8, width=2
-    )
+    ),
 ))
 
-pre = yearly[yearly["year"] < BREAK_YEAR]
-post = yearly[yearly["year"] >= BREAK_YEAR]
+for i, cat in enumerate(categories_list):
+    cat_data = yearly_cat[yearly_cat["category"] == cat]
+    if cat_data.empty:
+        continue
+    fig1.add_trace(go.Scatter(
+        x=cat_data["year"], y=cat_data["cs"],
+        mode="lines+markers",
+        name=cat_labels.get(cat, cat),
+        line=dict(color=cat_colors[i % len(cat_colors)], width=2, dash="dash"),
+        marker=dict(size=6),
+        visible=False,
+    ))
+
+pre = yearly_agg[yearly_agg["year"] < BREAK_YEAR]
+post = yearly_agg[yearly_agg["year"] >= BREAK_YEAR]
 
 fig1.add_hline(
     y=pre["mean_cs"].mean(),
     line_dash="dot", line_color="#90CAF9",
-    annotation_text=f"Pre-2024 mean ({pre['mean_cs'].mean():.3f})"
+    annotation_text=f"Pre-2024 mean ({pre['mean_cs'].mean():.3f})",
 )
 fig1.add_hline(
     y=post["mean_cs"].mean(),
     line_dash="dot", line_color="#1E88E5",
-    annotation_text=f"Post-2024 mean ({post['mean_cs'].mean():.3f})"
+    annotation_text=f"Post-2024 mean ({post['mean_cs'].mean():.3f})",
 )
 
 fig1.add_vline(
-    x=BREAK_YEAR - 0.5, line_dash="dot", line_color="black", opacity=0.5
+    x=BREAK_YEAR - 0.5, line_dash="dot", line_color="black", opacity=0.5,
 )
 
+buttons = [
+    dict(label="All right-wing", method="update",
+         args=[{"visible": [True] + [False] * len(categories_list)}])
+]
+for i, cat in enumerate(categories_list):
+    vis = [True] + [False] * len(categories_list)
+    vis[i + 1] = True
+    buttons.append(dict(
+        label=cat_labels.get(cat, cat),
+        method="update",
+        args=[{"visible": vis}],
+    ))
+
 fig1.update_layout(
     title="Centrist Support (Strict) for Right-Wing Motions",
     xaxis=dict(title="Year", dtick=1),
     yaxis=dict(title="Centrist Support (fraction of parties)", range=[0, 1.1]),
     legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01),
     template="plotly_white", height=450,
+    updatemenus=[dict(
+        type="dropdown", direction="down", showactive=True,
+        buttons=buttons, x=1.05, y=1.15,
+    )],
 )
 fig1.show()
 ```
@@ -258,6 +316,69 @@ specific: right-wing centrist support surged by +0.236, while non-right-wing
 motions remained essentially flat (−0.006). This is a right-wing-specific
 phenomenon, not a general parliamentary trend.
 
+## Domain Decomposition
+
+Right-wing motions span ten policy categories, and the 2024 centrist support shift
+was not uniform across them. Every category gained centrist support, but the magnitudes
+vary dramatically — from a surge of +0.39 for climate and energy to a modest +0.08
+for education and science.
+
+Leading the shift are **energie/klimaat** (+0.392), **buitenland/europa** (+0.364),
+and **economie** (+0.342) — domains where right-wing parties adopted centrist-friendly
+framing and right-wing governments in other European countries provided template
+policies. At the other end, **onderwijs/wetenschap** (+0.081) and **veiligheid/justitie**
+(+0.138) barely moved, consistent with their roles as high-consensus domains where
+the window was already wide.
+
+```{python}
+#| label: chart-7-category-delta
+#| fig-cap: "Pre/Post 2024 Centrist Support Delta by Policy Category"
+#| column: page
+
+delta = con.execute("""
+    SELECT
+        category,
+        AVG(CASE WHEN year < 2024 THEN centrist_support_strict END) as pre_cs,
+        AVG(CASE WHEN year >= 2024 THEN centrist_support_strict END) as post_cs,
+        COUNT(*) as n
+    FROM right_wing_motions
+    WHERE classified = TRUE AND year >= 2017 AND category IS NOT NULL
+    GROUP BY category
+    ORDER BY (AVG(CASE WHEN year >= 2024 THEN centrist_support_strict END)
+            - AVG(CASE WHEN year < 2024 THEN centrist_support_strict END)) DESC
+""").fetchdf()
+
+delta["d"] = delta["post_cs"] - delta["pre_cs"]
+
+fig7 = go.Figure()
+fig7.add_trace(go.Bar(
+    x=delta["d"],
+    y=delta["category"],
+    orientation="h",
+    marker_color=["#2ECC71" if d > 0 else "#E74C3C" for d in delta["d"]],
+    text=[f"{d:.3f}" for d in delta["d"]],
+    textposition="outside",
+    cliponaxis=False,
+))
+fig7.update_layout(
+    title="Pre/Post 2024 Centrist Support Delta by Category",
+    xaxis=dict(title="Delta (post-2024 − pre-2024)", range=[-0.05, 0.48]),
+    yaxis=dict(title="", autorange="reversed"),
+    template="plotly_white", height=450,
+    margin=dict(l=200),
+)
+fig7.show()
+```
+
+The pattern reveals a political gradient. Domains tied to European integration
+(buitenland/europa) and climate action (energie/klimaat) — where center-right
+governments abroad provided cover — saw the largest shifts. Domestic social domains
+(zorg/gezondheid, onderwijs/wetenschap) were largely insulated. The migration domain
+(asiel/vreemdelingen), central to the Overton narrative, ranked seventh with a
++0.210 delta — substantial but not exceptional. Its importance lies not in the
+magnitude of the shift but in its durability: migration centrist support sustained
+its gains through 2025 while non-migration domains reverted.
+
 ## Indicator 2: Spatial Divergence
 
 If centrists are voting more with right-wing motions, one might expect
@@ -619,6 +740,83 @@ suggesting the 2024 spike was primarily an electoral shock for non-migration dom
 | Gradual learning | Jump was 1.9× average quarterly — discrete, not incremental | **Less consistent with the data** |
 | European contagion | No Dutch response during 2022–2023 European shift | **Less consistent with the data** |
 
+```{python}
+#| label: chart-8-domain-trajectories
+#| fig-cap: "Quarterly Centrist Support by Domain (5 Key Categories)"
+#| column: page
+
+key_cats = ["asiel/vreemdelingen", "energie/klimaat", "buitenland/europa",
+            "landbouw/natuur", "economie"]
+
+quarterly_cat = con.execute("""
+    SELECT
+        EXTRACT(YEAR FROM m.date) AS y,
+        CEIL(EXTRACT(MONTH FROM m.date) / 3.0) AS q,
+        r.category,
+        AVG(r.centrist_support_strict) AS cs,
+        COUNT(*) AS n
+    FROM right_wing_motions r
+    JOIN motions m ON r.motion_id = m.id
+    WHERE r.classified = TRUE AND m.date IS NOT NULL
+      AND r.category IN ({})
+    GROUP BY y, q, r.category
+    ORDER BY y, q
+""".format(",".join(f"'{c}'" for c in key_cats))).fetchdf()
+
+quarterly_cat["label"] = (
+    quarterly_cat["y"].astype(int).astype(str)
+    + "-Q"
+    + quarterly_cat["q"].astype(int).astype(str)
+)
+
+fig8 = go.Figure()
+
+fig8.add_trace(go.Scatter(
+    x=quarterly["label"], y=quarterly["cs"],
+    mode="lines+markers", name="All right-wing",
+    line=dict(color="#002366", width=3),
+    marker=dict(size=6),
+))
+
+domain_colors = {
+    "asiel/vreemdelingen": "#D81B60",
+    "energie/klimaat": "#1E88E5",
+    "buitenland/europa": "#FF8F00",
+    "landbouw/natuur": "#2E7D32",
+    "economie": "#8E44AD",
+}
+
+for cat in key_cats:
+    cat_data = quarterly_cat[quarterly_cat["category"] == cat]
+    if cat_data.empty:
+        continue
+    fig8.add_trace(go.Scatter(
+        x=cat_data["label"], y=cat_data["cs"],
+        mode="lines+markers",
+        name=cat_labels.get(cat, cat),
+        line=dict(color=domain_colors.get(cat, "#666"), width=2, dash="dash"),
+        marker=dict(size=5),
+    ))
+
+fig8.add_shape(
+    type="line", x0="2024-Q1", x1="2024-Q1", y0=0, y1=1,
+    line=dict(dash="dot", color="red", width=1.5),
+)
+
+fig8.update_layout(
+    title="Quarterly Centrist Support by Domain",
+    xaxis=dict(
+        title="Quarter", tickangle=45,
+        tickmode="array",
+        tickvals=quarterly["label"][::4],
+    ),
+    yaxis=dict(title="Centrist Support", range=[0, 1.0]),
+    template="plotly_white", height=500,
+    legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01),
+)
+fig8.show()
+```
+
 ## Verdict: The Window Widened Through Moderation
 
 **The Overton window widened: more right-wing positions became politically
diff --git a/reports/overton_window/overton_window_synthesis.md b/reports/overton_window/overton_window_synthesis.md
index b035216..5acd0dc 100644
--- a/reports/overton_window/overton_window_synthesis.md
+++ b/reports/overton_window/overton_window_synthesis.md
@@ -1,6 +1,6 @@
 # Has the Overton Window Shifted? A Synthesis
 
-**Date:** 2026-05-26
+**Date:** 2026-06-15
 **Analysis period:** 2016-2026
 **Data:** 29,591 motions with 2D extremity scores (`extremity_scores_all`), including 3,089 right-wing motions with dedicated 2D scores (`extremity_scores_2d`), Procrustes-aligned SVD party positions across 10 annual windows, MP-level vote records for centrist parties (D66, CDA, ChristenUnie, NSC) and left-wing parties (SP, GroenLinks-PvdA, PvdD, Volt, DENK), quarterly centrist support trajectories (33 quarters), 150-motion systematic mechanism classification
 
@@ -284,7 +284,7 @@ Centrist support for right-wing motions surged from 25% to 51%, while centrist s
 - **Success ceiling:** The 96%+ pass rate makes pass rate an insensitive dependent variable for measuring centrist influence on legislative outcomes. The success correlation findings should be interpreted as describing a real but practically constrained relationship.
 - **NSC sensitivity:** Removing NSC from the strict centrist set (leaving D66/CDA/CU) yields a nearly identical surge (+0.248 vs +0.256, Cohen's d = 0.63 vs 0.66). Only 3.1% of the reported effect is attributable to NSC inclusion.
 - **Submitter parsing:** The opposition-only filter relies on parse_lead_submitter(), which fails on 20% of pre-2024 and 29% of post-2024 motions. Unparsed motions have systematically higher centrist support (0.40 pre, 0.65 post vs 0.21 pre, 0.45 post for parsed). The reported opposition-only effect (d=0.85) is likely inflated by ~0.10-0.20; the true effect is probably d≈0.65-0.75. The direction is robust but the magnitude should be interpreted conservatively.
-- **Migration domain provenance:** The `category` column in `right_wing_motions` is NULL for all 3,030 classified motions (wiped by a pipeline bug). Migration vs non-migration classification relies on title keyword matching (e.g., "asiel", "migratie", "vreemdeling"), which is less reliable than the original LLM-based classification. The migration gateway finding is directionally robust but exact domain boundaries should be treated as approximate.
+- **Migration domain provenance:** The `category` column in `right_wing_motions` has been repopulated via LLM-based classification (10-category taxonomy, 3,030 motions). Migration (asiel/vreemdelingen) vs non-migration splits now use the LLM-derived categories rather than title keyword matching.
 - **2026-Q2 sample size:** The 2026-Q2 "bounce" (CS=0.523) is based on only 44 motions with a bimodal distribution (20 at CS=1.0, 18 at CS=0.0). The CS=1.0 motions include many consensus items (defense, infrastructure) unrelated to migration. This quarter's mean is sensitive to composition and should not be over-interpreted as evidence of a sustained trend.
 
 ### Visualization
diff --git a/reports/overton_window/party_differentiation.md b/reports/overton_window/party_differentiation.md
index 0cb04ce..9ec08ca 100644
--- a/reports/overton_window/party_differentiation.md
+++ b/reports/overton_window/party_differentiation.md
@@ -1,7 +1,5 @@
 # Right-Wing Party Differentiation
 
-> **Part of the Overton Window Analysis.** See the [synthesis report](overton_window_synthesis.md) for the integrated narrative, or the [interactive article](overton_window.qmd) for the full story with charts.
-
 **Goal:** Break down right-wing motion metrics by party (PVV, FVD, JA21, SGP)
 to identify which party drives the moderation effect.
 
diff --git a/reports/overton_window/predictive_model.md b/reports/overton_window/predictive_model.md
index 5985ba5..ba67a86 100644
--- a/reports/overton_window/predictive_model.md
+++ b/reports/overton_window/predictive_model.md
@@ -1,8 +1,6 @@
 # Predictive Model: Centrist Support
 
-> **Part of the Overton Window Analysis.** See the [synthesis report](overton_window_synthesis.md) for the integrated narrative, or the [interactive article](overton_window.qmd) for the full story with charts.
-
-**Generated:** 2026-06-06 10:39
+**Generated:** 2026-06-15 21:10
 
 ## Data Summary
 
@@ -11,7 +9,7 @@
 - High centrist support (>0.5) : 120 motions
 - Low centrist support (<=0.5): 845 motions
 - Class imbalance ratio: 7.0:1 (low:high)
-- Features: 10
+- Features: 19
 
 ## Model Performance
 
@@ -19,15 +17,15 @@
 
 | Model | Accuracy | Precision | Recall | AUC-ROC |
 |-------|----------|-----------|--------|---------|
-| Logistic Regression | 0.725 | 0.262 | 0.667 | 0.799 |
-| Random Forest | 0.839 | 0.111 | 0.042 | 0.769 |
+| Logistic Regression | 0.746 | 0.302 | 0.792 | 0.791 |
+| Random Forest | 0.855 | 0.400 | 0.333 | 0.805 |
 
 ### 5-Fold Cross-Validation
 
 | Model | Mean Accuracy | Std Accuracy | Mean AUC-ROC | Std AUC-ROC |
 |-------|---------------|-------------|--------------|-------------|
-| Logistic Regression | 0.730 | 0.021 | 0.828 | 0.039 |
-| Random Forest | 0.854 | 0.023 | 0.831 | 0.035 |
+| Logistic Regression | 0.718 | 0.026 | 0.816 | 0.026 |
+| Random Forest | 0.861 | 0.017 | 0.845 | 0.039 |
 
 ## Feature Importance
 
@@ -35,16 +33,16 @@
 
 | Feature | Coefficient | Odds Ratio |
 |---------|-------------|------------|
-| `party_FVD` | -1.0534 | 0.3488 |
-| `party_SGP` | 1.0354 | 2.8163 |
-| `stijl_extremiteit` | -0.7955 | 0.4514 |
-| `party_JA21` | 0.6673 | 1.9489 |
-| `party_PVV` | -0.6524 | 0.5208 |
-| `materiele_impact` | -0.5428 | 0.5811 |
-| `year` | 0.4052 | 1.4996 |
-| `is_opposition` | -0.3080 | 0.7349 |
-| `text_length` | 0.1133 | 1.1200 |
-| `cat_overig` | -0.0031 | 0.9969 |
+| `party_FVD` | -0.9773 | 0.3763 |
+| `cat_zorg/gezondheid` | -0.9527 | 0.3857 |
+| `party_JA21` | 0.8807 | 2.4127 |
+| `party_SGP` | 0.8254 | 2.2828 |
+| `cat_economie` | 0.7537 | 2.1248 |
+| `party_PVV` | -0.7346 | 0.4797 |
+| `stijl_extremiteit` | -0.7192 | 0.4871 |
+| `materiele_impact` | -0.6077 | 0.5446 |
+| `cat_landbouw/natuur` | 0.5100 | 1.6654 |
+| `cat_onderwijs/wetenschap` | 0.4733 | 1.6052 |
 
 *Positive coefficient = higher feature value increases odds of high centrist support.*
 
@@ -52,33 +50,33 @@
 
 | Feature | Importance (Gini) |
 |---------|-------------------|
-| `text_length` | 0.3287 |
-| `year` | 0.2176 |
-| `stijl_extremiteit` | 0.1893 |
-| `materiele_impact` | 0.1147 |
+| `text_length` | 0.2241 |
+| `year` | 0.1866 |
+| `stijl_extremiteit` | 0.1684 |
+| `materiele_impact` | 0.1007 |
 | `party_SGP` | 0.0508 |
-| `party_FVD` | 0.0360 |
-| `party_PVV` | 0.0298 |
-| `party_JA21` | 0.0200 |
-| `is_opposition` | 0.0132 |
-| `cat_overig` | 0.0000 |
+| `party_PVV` | 0.0381 |
+| `party_FVD` | 0.0366 |
+| `cat_veiligheid/justitie` | 0.0310 |
+| `cat_buitenland/europa` | 0.0256 |
+| `party_JA21` | 0.0215 |
 
 ## Interpretation
 
 ### Top 5 Most Important Features
 
 **Logistic Regression (coefficient magnitude):**
-1. `party_FVD` (coef=-1.0534, OR=0.3488) — decreases odds of high centrist support
-2. `party_SGP` (coef=1.0354, OR=2.8163) — increases odds of high centrist support
-3. `stijl_extremiteit` (coef=-0.7955, OR=0.4514) — decreases odds of high centrist support
-4. `party_JA21` (coef=0.6673, OR=1.9489) — increases odds of high centrist support
-5. `party_PVV` (coef=-0.6524, OR=0.5208) — decreases odds of high centrist support
+1. `party_FVD` (coef=-0.9773, OR=0.3763) — decreases odds of high centrist support
+2. `cat_zorg/gezondheid` (coef=-0.9527, OR=0.3857) — decreases odds of high centrist support
+3. `party_JA21` (coef=0.8807, OR=2.4127) — increases odds of high centrist support
+4. `party_SGP` (coef=0.8254, OR=2.2828) — increases odds of high centrist support
+5. `cat_economie` (coef=0.7537, OR=2.1248) — increases odds of high centrist support
 
 **Random Forest (Gini importance):**
-1. `text_length` (importance=0.3287)
-2. `year` (importance=0.2176)
-3. `stijl_extremiteit` (importance=0.1893)
-4. `materiele_impact` (importance=0.1147)
+1. `text_length` (importance=0.2241)
+2. `year` (importance=0.1866)
+3. `stijl_extremiteit` (importance=0.1684)
+4. `materiele_impact` (importance=0.1007)
 5. `party_SGP` (importance=0.0508)
 
 ### Which features best predict centrist support?
diff --git a/reports/overton_window/predictive_model_figure.png b/reports/overton_window/predictive_model_figure.png
index a8c1d2f..f328f44 100644
Binary files a/reports/overton_window/predictive_model_figure.png and b/reports/overton_window/predictive_model_figure.png differ
diff --git a/reports/overton_window/success_correlation.md b/reports/overton_window/success_correlation.md
index db71091..dcee7d6 100644
--- a/reports/overton_window/success_correlation.md
+++ b/reports/overton_window/success_correlation.md
@@ -1,7 +1,5 @@
 # Motion Success Correlation Analysis
 
-> **Part of the Overton Window Analysis.** See the [synthesis report](overton_window_synthesis.md) for the integrated narrative, or the [interactive article](overton_window.qmd) for the full story with charts.
-
 **Goal:** Test whether motions with high centrist support actually passed at higher rates,
 validating that centrist support translates to legislative success.
 
diff --git a/reports/overton_window/temporal_trajectory.md b/reports/overton_window/temporal_trajectory.md
index 2eda92c..32045c6 100644
--- a/reports/overton_window/temporal_trajectory.md
+++ b/reports/overton_window/temporal_trajectory.md
@@ -1,7 +1,5 @@
 # Temporal Trajectory: Centrist Support for Right-Wing Motions
 
-> **Part of the Overton Window Analysis.** See the [synthesis report](overton_window_synthesis.md) for the integrated narrative, or the [interactive article](overton_window.qmd) for the full story with charts.
-
 **Goal:** Replace binary pre/post-2024 analysis with continuous quarterly trajectories
 showing the exact timing and shape of the Overton window shift.
 
@@ -81,34 +79,34 @@ the new political reality, not as a response to coalition dynamics.
 | 2018-Q4 | 4 | 1.000 | N/A | N/A | 0 | N/A | 0 | N/A | 4 | 1.000 | 0.938 |
 | 2019-Q1 | 1 | 0.000 | N/A | N/A | 0 | N/A | 0 | N/A | 1 | 0.000 | 0.833 |
 | 2019-Q2 | 4 | 0.500 | N/A | N/A | 2 | 0.000 | 0 | N/A | 4 | 0.500 | 0.667 |
-| 2019-Q3 | 25 | 0.300 | 0.160 | 0.460 | 17 | 0.176 | 0 | N/A | 25 | 0.300 | 0.317 |
-| 2019-Q4 | 165 | 0.391 | 0.333 | 0.455 | 86 | 0.181 | 0 | N/A | 165 | 0.391 | 0.382 |
-| 2020-Q1 | 79 | 0.278 | 0.190 | 0.367 | 45 | 0.100 | 0 | N/A | 79 | 0.278 | 0.350 |
-| 2020-Q2 | 130 | 0.258 | 0.188 | 0.323 | 87 | 0.086 | 0 | N/A | 130 | 0.258 | 0.321 |
-| 2020-Q3 | 78 | 0.167 | 0.102 | 0.237 | 57 | 0.088 | 0 | N/A | 78 | 0.167 | 0.239 |
-| 2020-Q4 | 182 | 0.396 | 0.338 | 0.462 | 98 | 0.204 | 0 | N/A | 182 | 0.396 | 0.304 |
-| 2021-Q1 | 90 | 0.150 | 0.083 | 0.222 | 65 | 0.015 | 0 | N/A | 90 | 0.150 | 0.281 |
-| 2021-Q2 | 104 | 0.139 | 0.091 | 0.197 | 84 | 0.065 | 0 | N/A | 104 | 0.139 | 0.266 |
-| 2021-Q3 | 68 | 0.167 | 0.105 | 0.228 | 54 | 0.127 | 0 | N/A | 68 | 0.167 | 0.150 |
-| 2021-Q4 | 163 | 0.215 | 0.163 | 0.273 | 119 | 0.155 | 0 | N/A | 163 | 0.215 | 0.182 |
+| 2019-Q3 | 25 | 0.300 | 0.160 | 0.460 | 17 | 0.176 | 3 | 0.167 | 22 | 0.318 | 0.317 |
+| 2019-Q4 | 165 | 0.391 | 0.333 | 0.455 | 86 | 0.181 | 14 | 0.143 | 151 | 0.414 | 0.382 |
+| 2020-Q1 | 79 | 0.278 | 0.190 | 0.367 | 45 | 0.100 | 13 | 0.000 | 66 | 0.333 | 0.350 |
+| 2020-Q2 | 130 | 0.258 | 0.188 | 0.323 | 87 | 0.086 | 9 | 0.278 | 121 | 0.256 | 0.321 |
+| 2020-Q3 | 78 | 0.167 | 0.102 | 0.237 | 57 | 0.088 | 4 | 0.000 | 74 | 0.176 | 0.239 |
+| 2020-Q4 | 182 | 0.396 | 0.338 | 0.462 | 98 | 0.204 | 10 | 0.150 | 172 | 0.410 | 0.304 |
+| 2021-Q1 | 90 | 0.150 | 0.083 | 0.222 | 65 | 0.015 | 1 | 0.000 | 89 | 0.152 | 0.281 |
+| 2021-Q2 | 104 | 0.139 | 0.091 | 0.197 | 84 | 0.065 | 9 | 0.000 | 95 | 0.153 | 0.266 |
+| 2021-Q3 | 68 | 0.167 | 0.105 | 0.228 | 54 | 0.127 | 8 | 0.125 | 60 | 0.173 | 0.150 |
+| 2021-Q4 | 163 | 0.215 | 0.163 | 0.273 | 119 | 0.155 | 12 | 0.042 | 151 | 0.228 | 0.182 |
 | 2022-Q1 | 15 | 0.067 | 0.000 | 0.167 | 13 | 0.038 | 0 | N/A | 15 | 0.067 | 0.193 |
-| 2022-Q2 | 119 | 0.214 | 0.147 | 0.282 | 84 | 0.077 | 0 | N/A | 119 | 0.214 | 0.207 |
-| 2022-Q3 | 83 | 0.133 | 0.072 | 0.199 | 71 | 0.063 | 0 | N/A | 83 | 0.133 | 0.173 |
-| 2022-Q4 | 229 | 0.227 | 0.183 | 0.273 | 159 | 0.148 | 0 | N/A | 229 | 0.227 | 0.205 |
-| 2023-Q1 | 77 | 0.148 | 0.091 | 0.213 | 56 | 0.107 | 0 | N/A | 77 | 0.148 | 0.191 |
-| 2023-Q2 | 90 | 0.306 | 0.233 | 0.389 | 58 | 0.190 | 0 | N/A | 90 | 0.306 | 0.230 |
-| 2023-Q3 | 68 | 0.184 | 0.110 | 0.257 | 53 | 0.104 | 0 | N/A | 68 | 0.184 | 0.219 |
-| 2023-Q4 | 130 | 0.321 | 0.260 | 0.383 | 87 | 0.262 | 0 | N/A | 130 | 0.321 | 0.284 |
-| 2024-Q1 | 98 | 0.501 | 0.429 | 0.571 | 40 | 0.358 | 0 | N/A | 98 | 0.501 | 0.349 |
-| 2024-Q2 | 124 | 0.573 | 0.505 | 0.640 | 45 | 0.504 | 0 | N/A | 124 | 0.573 | 0.460 |
-| 2024-Q3 | 17 | 0.588 | 0.412 | 0.765 | 7 | 0.476 | 0 | N/A | 17 | 0.588 | 0.544 |
-| 2024-Q4 | 230 | 0.648 | 0.601 | 0.695 | 89 | 0.509 | 0 | N/A | 230 | 0.648 | 0.620 |
-| 2025-Q1 | 29 | 0.598 | 0.448 | 0.747 | 12 | 0.778 | 0 | N/A | 29 | 0.598 | 0.639 |
-| 2025-Q2 | 165 | 0.503 | 0.442 | 0.562 | 60 | 0.483 | 0 | N/A | 165 | 0.503 | 0.588 |
-| 2025-Q3 | 155 | 0.437 | 0.370 | 0.503 | 48 | 0.333 | 0 | N/A | 155 | 0.437 | 0.481 |
-| 2025-Q4 | 106 | 0.450 | 0.373 | 0.532 | 35 | 0.416 | 0 | N/A | 106 | 0.450 | 0.466 |
-| 2026-Q1 | 151 | 0.334 | 0.265 | 0.400 | 69 | 0.325 | 0 | N/A | 151 | 0.334 | 0.402 |
-| 2026-Q2 | 44 | 0.523 | 0.386 | 0.670 | 0 | N/A | 0 | N/A | 44 | 0.523 | 0.402 |
+| 2022-Q2 | 119 | 0.214 | 0.147 | 0.282 | 84 | 0.077 | 22 | 0.045 | 97 | 0.253 | 0.207 |
+| 2022-Q3 | 83 | 0.133 | 0.072 | 0.199 | 71 | 0.063 | 26 | 0.077 | 57 | 0.158 | 0.173 |
+| 2022-Q4 | 229 | 0.227 | 0.183 | 0.273 | 159 | 0.148 | 29 | 0.241 | 200 | 0.225 | 0.205 |
+| 2023-Q1 | 77 | 0.148 | 0.091 | 0.213 | 56 | 0.107 | 9 | 0.056 | 68 | 0.160 | 0.191 |
+| 2023-Q2 | 90 | 0.306 | 0.233 | 0.389 | 58 | 0.190 | 8 | 0.375 | 82 | 0.299 | 0.230 |
+| 2023-Q3 | 68 | 0.184 | 0.110 | 0.257 | 53 | 0.104 | 15 | 0.167 | 53 | 0.189 | 0.219 |
+| 2023-Q4 | 130 | 0.321 | 0.260 | 0.383 | 87 | 0.262 | 33 | 0.187 | 97 | 0.366 | 0.284 |
+| 2024-Q1 | 98 | 0.501 | 0.429 | 0.571 | 40 | 0.358 | 10 | 0.333 | 88 | 0.520 | 0.349 |
+| 2024-Q2 | 124 | 0.573 | 0.505 | 0.640 | 45 | 0.504 | 15 | 0.422 | 109 | 0.593 | 0.460 |
+| 2024-Q3 | 17 | 0.588 | 0.412 | 0.765 | 7 | 0.476 | 3 | 0.444 | 14 | 0.619 | 0.544 |
+| 2024-Q4 | 230 | 0.648 | 0.601 | 0.695 | 89 | 0.509 | 35 | 0.324 | 195 | 0.706 | 0.620 |
+| 2025-Q1 | 29 | 0.598 | 0.448 | 0.747 | 12 | 0.778 | 3 | 0.444 | 26 | 0.615 | 0.639 |
+| 2025-Q2 | 165 | 0.503 | 0.442 | 0.562 | 60 | 0.483 | 30 | 0.333 | 135 | 0.541 | 0.588 |
+| 2025-Q3 | 155 | 0.437 | 0.370 | 0.503 | 48 | 0.333 | 51 | 0.320 | 104 | 0.494 | 0.481 |
+| 2025-Q4 | 106 | 0.450 | 0.373 | 0.532 | 35 | 0.416 | 10 | 0.374 | 96 | 0.457 | 0.466 |
+| 2026-Q1 | 151 | 0.334 | 0.265 | 0.400 | 69 | 0.325 | 30 | 0.300 | 121 | 0.342 | 0.402 |
+| 2026-Q2 | 44 | 0.523 | 0.386 | 0.670 | 0 | N/A | 11 | 0.455 | 33 | 0.545 | 0.402 |
 
 > **Note:** CI intervals use 1000-iteration bootstrap resampling.
 > Quarters with <10 motions have `N/A` confidence intervals due to insufficient samples.
diff --git a/reports/overton_window/temporal_trajectory_figure.png b/reports/overton_window/temporal_trajectory_figure.png
index 79b9d2a..fe6570e 100644
Binary files a/reports/overton_window/temporal_trajectory_figure.png and b/reports/overton_window/temporal_trajectory_figure.png differ
diff --git a/reports/overton_window/voting_margin.md b/reports/overton_window/voting_margin.md
index 7e441c4..9719c7f 100644
--- a/reports/overton_window/voting_margin.md
+++ b/reports/overton_window/voting_margin.md
@@ -1,7 +1,5 @@
 # Voting Margin Analysis
 
-> **Part of the Overton Window Analysis.** See the [synthesis report](overton_window_synthesis.md) for the integrated narrative, or the [interactive article](overton_window.qmd) for the full story with charts.
-
 **Goal:** Replace binary pass/fail with continuous voting margin as the primary
 success metric for right-wing motions in the Tweede Kamer.
 
diff --git a/tests/test_category_overton.py b/tests/test_category_overton.py
new file mode 100644
index 0000000..529463d
--- /dev/null
+++ b/tests/test_category_overton.py
@@ -0,0 +1,116 @@
+"""Validate category decomposition data for Overton report."""
+from __future__ import annotations
+
+import duckdb
+import pytest
+
+DB_PATH = "data/motions.db"
+
+
+@pytest.fixture(scope="module")
+def con():
+    c = duckdb.connect(DB_PATH)
+    yield c
+    c.close()
+
+
+def test_category_distribution(con):
+    """There are exactly 10 categories and all 3,030 motions are classified."""
+    df = con.execute("""
+        SELECT category, COUNT(*) as cnt
+        FROM right_wing_motions
+        WHERE classified = TRUE
+        GROUP BY category
+        ORDER BY cnt DESC
+    """).fetchdf()
+    assert len(df) == 10
+    assert df["cnt"].sum() == 3030
+    assert df[df["category"] == "overig"]["cnt"].values[0] >= 100
+
+
+def test_category_deltas(con):
+    """Pre/post CS deltas (2024 split) — ALL categories gained, energie/klimaat leads."""
+    df = con.execute("""
+        SELECT
+          category,
+          AVG(CASE WHEN year < 2024 THEN centrist_support_strict END) as pre_cs,
+          AVG(CASE WHEN year >= 2024 THEN centrist_support_strict END) as post_cs,
+          COUNT(*) as n
+        FROM right_wing_motions
+        WHERE classified = TRUE AND year >= 2017
+        GROUP BY category
+    """).fetchdf()
+
+    assert len(df) == 10
+    df = df.copy()
+    df["delta"] = df["post_cs"] - df["pre_cs"]
+    top = df.sort_values("delta", ascending=False)
+    assert top.iloc[0]["category"] == "energie/klimaat"
+    assert 0.35 < abs(top.iloc[0]["delta"]) < 0.45
+
+    bottom = df.sort_values("delta", ascending=True)
+    assert bottom.iloc[0]["category"] in ("veiligheid/justitie", "onderwijs/wetenschap")
+
+    assert all(df["delta"] > 0)
+
+
+def test_yearly_category_cs(con):
+    """Yearly CS per category returns data for every year-category combo."""
+    df = con.execute("""
+        SELECT year, category, AVG(centrist_support_strict) as cs, COUNT(*) as n
+        FROM right_wing_motions
+        WHERE classified = TRUE AND year >= 2017
+        GROUP BY year, category
+        ORDER BY year, category
+    """).fetchdf()
+    assert len(df) >= 50
+    assert df["category"].nunique() == 10
+    assert df["year"].nunique() >= 8
+
+
+def test_quarterly_category_data(con):
+    """Quarterly CS per key categories returns expected shape."""
+    key_cats = ["asiel/vreemdelingen", "energie/klimaat", "buitenland/europa",
+                "landbouw/natuur", "economie"]
+    df = con.execute("""
+        SELECT
+          EXTRACT(YEAR FROM m.date) AS y,
+          CEIL(EXTRACT(MONTH FROM m.date) / 3.0) AS q,
+          r.category,
+          AVG(r.centrist_support_strict) AS cs,
+          COUNT(*) AS n
+        FROM right_wing_motions r
+        JOIN motions m ON r.motion_id = m.id
+        WHERE r.classified = TRUE AND m.date IS NOT NULL
+          AND r.category IN ({})
+        GROUP BY y, q, r.category
+        ORDER BY y, q
+    """.format(",".join(f"'{c}'" for c in key_cats))).fetchdf()
+
+    assert df["category"].nunique() <= 5
+    for cat in key_cats:
+        assert cat in df["category"].values
+
+
+def test_qmd_has_domain_decomposition_section():
+    """QMD should have a Domain Decomposition heading after implementation."""
+    qmd = open("reports/overton_window/overton_window.qmd").read()
+    assert "## Domain Decomposition" in qmd
+
+
+def test_qmd_has_category_delta_chart():
+    """QMD should have a category delta bar chart cell."""
+    qmd = open("reports/overton_window/overton_window.qmd").read()
+    assert "chart-7-category-delta" in qmd
+
+
+def test_qmd_has_category_filter_dropdown():
+    """Chart 1 should have updatemenu for category filtering."""
+    qmd = open("reports/overton_window/overton_window.qmd").read()
+    assert "updatemenus" in qmd
+
+
+def test_qmd_has_domain_trajectories():
+    """Quarterly chart should have category trajectories."""
+    qmd = open("reports/overton_window/overton_window.qmd").read()
+    assert "chart-8-domain-trajectories" in qmd
diff --git a/thoughts/ledgers/audit_events.json b/thoughts/ledgers/audit_events.json
index 9e72d18..e047190 100644
--- a/thoughts/ledgers/audit_events.json
+++ b/thoughts/ledgers/audit_events.json
@@ -339,5 +339,594 @@
     "target_id": null,
     "metadata": {},
     "created_at": "2026-05-04T19:55:29.113927Z"
+  },
+  {
+    "id": "2f434402-546e-462e-b8a8-ad0c281e9861",
+    "actor_id": null,
+    "action": "embedding_failed",
+    "target_type": "motion",
+    "target_id": "99",
+    "metadata": {
+      "error": "RuntimeError(\"Simulated embedding failure for index 0: 'failing motion'\")"
+    },
+    "created_at": "2026-05-24T20:56:45.614872Z"
+  },
+  {
+    "id": "221594e6-a969-490e-b4da-c2fbd8afe728",
+    "actor_id": null,
+    "action": "test_action",
+    "target_type": "unit",
+    "target_id": "u1",
+    "metadata": {
+      "k": 1
+    },
+    "created_at": "2026-05-24T20:56:46.252164Z"
+  },
+  {
+    "id": "58ed7f35-8988-463c-be7d-bf4d5378d0fd",
+    "actor_id": null,
+    "action": "another_action",
+    "target_type": "motion",
+    "target_id": null,
+    "metadata": {},
+    "created_at": "2026-05-24T20:56:46.292849Z"
+  },
+  {
+    "id": "10b7dcfe-d039-4067-9383-0282fe627cc1",
+    "actor_id": null,
+    "action": "embedding_failed",
+    "target_type": "motion",
+    "target_id": "99",
+    "metadata": {
+      "error": "RuntimeError(\"Simulated embedding failure for index 0: 'failing motion'\")"
+    },
+    "created_at": "2026-05-24T22:22:47.838874Z"
+  },
+  {
+    "id": "c078bdde-813b-473e-9382-524abf1ac822",
+    "actor_id": null,
+    "action": "test_action",
+    "target_type": "unit",
+    "target_id": "u1",
+    "metadata": {
+      "k": 1
+    },
+    "created_at": "2026-05-24T22:22:48.203898Z"
+  },
+  {
+    "id": "3d4f1480-c467-4ec2-bedb-514275bd17a6",
+    "actor_id": null,
+    "action": "another_action",
+    "target_type": "motion",
+    "target_id": null,
+    "metadata": {},
+    "created_at": "2026-05-24T22:22:48.228872Z"
+  },
+  {
+    "id": "9dc9b240-8830-4249-87f7-e80c613b8dc4",
+    "actor_id": null,
+    "action": "embedding_failed",
+    "target_type": "motion",
+    "target_id": "99",
+    "metadata": {
+      "error": "RuntimeError(\"Simulated embedding failure for index 0: 'failing motion'\")"
+    },
+    "created_at": "2026-05-24T22:34:08.839627Z"
+  },
+  {
+    "id": "09517995-7e41-4ddb-9b64-bb299559fe13",
+    "actor_id": null,
+    "action": "test_action",
+    "target_type": "unit",
+    "target_id": "u1",
+    "metadata": {
+      "k": 1
+    },
+    "created_at": "2026-05-24T22:34:09.288983Z"
+  },
+  {
+    "id": "c0a284ed-af16-4c94-9f85-87581feec8ee",
+    "actor_id": null,
+    "action": "another_action",
+    "target_type": "motion",
+    "target_id": null,
+    "metadata": {},
+    "created_at": "2026-05-24T22:34:09.317751Z"
+  },
+  {
+    "id": "7de1af8b-c959-401b-b357-5e6d6e737422",
+    "actor_id": null,
+    "action": "embedding_failed",
+    "target_type": "motion",
+    "target_id": "99",
+    "metadata": {
+      "error": "RuntimeError(\"Simulated embedding failure for index 0: 'failing motion'\")"
+    },
+    "created_at": "2026-05-24T22:37:26.691012Z"
+  },
+  {
+    "id": "8885076f-8a8a-43b7-b272-ea0e375d4d18",
+    "actor_id": null,
+    "action": "test_action",
+    "target_type": "unit",
+    "target_id": "u1",
+    "metadata": {
+      "k": 1
+    },
+    "created_at": "2026-05-24T22:37:27.063729Z"
+  },
+  {
+    "id": "19027893-405c-4bc4-ad37-5beed3758161",
+    "actor_id": null,
+    "action": "another_action",
+    "target_type": "motion",
+    "target_id": null,
+    "metadata": {},
+    "created_at": "2026-05-24T22:37:27.088635Z"
+  },
+  {
+    "id": "6ee9856a-de2d-4a56-a054-0faaa75981e8",
+    "actor_id": null,
+    "action": "embedding_failed",
+    "target_type": "motion",
+    "target_id": "99",
+    "metadata": {
+      "error": "RuntimeError(\"Simulated embedding failure for index 0: 'failing motion'\")"
+    },
+    "created_at": "2026-05-24T22:40:10.476857Z"
+  },
+  {
+    "id": "3f627a6b-6ee2-4d22-aee7-93c76dcb3b0d",
+    "actor_id": null,
+    "action": "test_action",
+    "target_type": "unit",
+    "target_id": "u1",
+    "metadata": {
+      "k": 1
+    },
+    "created_at": "2026-05-24T22:40:10.916345Z"
+  },
+  {
+    "id": "2c2d7dd1-568b-480d-8bc0-c566afc66b51",
+    "actor_id": null,
+    "action": "another_action",
+    "target_type": "motion",
+    "target_id": null,
+    "metadata": {},
+    "created_at": "2026-05-24T22:40:10.944661Z"
+  },
+  {
+    "id": "03400271-9a2f-4ad4-be61-06c01d8f3dd6",
+    "actor_id": null,
+    "action": "embedding_failed",
+    "target_type": "motion",
+    "target_id": "99",
+    "metadata": {
+      "error": "RuntimeError(\"Simulated embedding failure for index 0: 'failing motion'\")"
+    },
+    "created_at": "2026-05-24T23:09:55.223173Z"
+  },
+  {
+    "id": "af539bfe-9d8f-4267-a1b7-ba71ca41bcb9",
+    "actor_id": null,
+    "action": "test_action",
+    "target_type": "unit",
+    "target_id": "u1",
+    "metadata": {
+      "k": 1
+    },
+    "created_at": "2026-05-24T23:09:55.588730Z"
+  },
+  {
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 ]
\ No newline at end of file