fix(overton): strict centrist definition + left support analysis

- Reclassified centrist to {D66, CDA, CU, NSC} — removing VVD/BBB
  which are center-right coalition partners
- Added centrist_support_strict (0.251→0.507, d=+0.65), center_right_support,
  and left_support_mp columns via migration script
- Figure 1 now shows center-right (VVD/BBB) support as orange dashed line
- New Figure 3: bar chart of left-party support for right-wing motions
  (0.268→0.202, left opposition hardened)
- New report Section 6 covering left-wing support trends
- All analysis now uses strict centrist definition throughout

analysis/right_wing/migrate_mp_level_metrics.py

@@ -1,15 +1,32 @@
-"""Add MP-weighted centrist_support column to right_wing_motions.
+"""Add MP-weighted support columns to right_wing_motions.
 
-The existing centrist_support is party-bloc-level (fraction of centrist
-parties where >=50% of MPs voted voor). This adds centrist_support_mp which
-is the fraction of individual centrist MPs who voted voor, weighted by party
-size.
+Adds centrist_support_mp, centrist_support_strict, center_right_support,
+and left_support_mp — all computed as the fraction of individual MPs
+within each party set who voted 'voor'.
 """
+from __future__ import annotations
 
-import duckdb
+import sys
 from pathlib import Path
 
+ROOT = Path(__file__).parent.parent.parent.resolve()
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))
+
+import duckdb
+
+from analysis.config import CANONICAL_LEFT
+
 CANONICAL_CENTRIST = frozenset({"VVD", "D66", "CDA", "NSC", "BBB", "CU"})
+CANONICAL_CENTRIST_STRICT = frozenset({"D66", "CDA", "CU", "NSC"})
+CANONICAL_CENTER_RIGHT = frozenset({"VVD", "BBB"})
+
+COLUMNS = [
+    ("centrist_support_mp", CANONICAL_CENTRIST),
+    ("centrist_support_strict", CANONICAL_CENTRIST_STRICT),
+    ("center_right_support", CANONICAL_CENTER_RIGHT),
+    ("left_support_mp", CANONICAL_LEFT),
+]
 
 
 def compute_mp_support(
@@ -51,16 +68,18 @@ def main(db_path: str = "data/motions.db"):
         pv = mv.setdefault(party, {"voor": 0, "tegen": 0, "afwezig": 0})
         pv[vote] = pv.get(vote, 0) + n
 
-    # Add column
-    col_check = con.execute(
-        "SELECT column_name FROM information_schema.columns "
-        "WHERE table_name = 'right_wing_motions' AND column_name = 'centrist_support_mp'"
-    ).fetchone()
-    if col_check is None:
-        con.execute(
-            "ALTER TABLE right_wing_motions ADD COLUMN centrist_support_mp DOUBLE"
-        )
-        print("Added centrist_support_mp column")
+    # Add columns if missing
+    for col_name, _party_set in COLUMNS:
+        col_check = con.execute(
+            "SELECT column_name FROM information_schema.columns "
+            "WHERE table_name = 'right_wing_motions' AND column_name = ?",
+            [col_name],
+        ).fetchone()
+        if col_check is None:
+            con.execute(
+                f"ALTER TABLE right_wing_motions ADD COLUMN {col_name} DOUBLE"
+            )
+            print(f"Added {col_name} column")
 
     # Update rows
     rows = con.execute(
@@ -74,11 +93,12 @@ def main(db_path: str = "data/motions.db"):
         if votes is None:
             skipped += 1
             continue
-        cs_mp = compute_mp_support(votes, CANONICAL_CENTRIST)
-        con.execute(
-            "UPDATE right_wing_motions SET centrist_support_mp = ? WHERE motion_id = ?",
-            [cs_mp, motion_id],
-        )
+        for col_name, party_set in COLUMNS:
+            val = compute_mp_support(votes, party_set)
+            con.execute(
+                f"UPDATE right_wing_motions SET {col_name} = ? WHERE motion_id = ?",
+                [val, motion_id],
+            )
         updated += 1
 
     con.close()

analysis/right_wing/overton_breakpoint_analysis.py

@@ -100,7 +100,8 @@ def compute_yearly_rw_metrics(con: duckdb.DuckDBPyConnection) -> dict[int, dict]
             r.motion_id,
             r.year,
             r.title,
-            r.centrist_support,
+            r.centrist_support_strict,
+            r.center_right_support,
             r.right_support,
             r.left_opposition,
             r.category,
@@ -118,7 +119,8 @@ def compute_yearly_rw_metrics(con: duckdb.DuckDBPyConnection) -> dict[int, dict]
     yearly: dict[int, dict[str, Any]] = {}
     for year in range(YEAR_MIN, YEAR_MAX + 1):
         yearly[year] = {
-            "centrist_support": [],
+            "centrist_support_strict": [],
+            "center_right_support": [],
             "right_support": [],
             "left_opposition": [],
             "extremity": [],
@@ -128,10 +130,11 @@ def compute_yearly_rw_metrics(con: duckdb.DuckDBPyConnection) -> dict[int, dict]
             "motion_ids": [],
         }
 
-    for mid, year, title, cs, rs, lo, cat, ext, vr_json, wm in rows:
+    for mid, year, title, cst, crs, rs, lo, cat, ext, vr_json, wm in rows:
         if year is None or year < YEAR_MIN or year > YEAR_MAX:
             continue
-        yearly[year]["centrist_support"].append(cs if cs is not None else np.nan)
+        yearly[year]["centrist_support_strict"].append(cst if cst is not None else np.nan)
+        yearly[year]["center_right_support"].append(crs if crs is not None else np.nan)
         yearly[year]["right_support"].append(rs if rs is not None else np.nan)
         yearly[year]["left_opposition"].append(lo if lo is not None else np.nan)
         yearly[year]["extremity"].append(ext if ext is not None else np.nan)
@@ -286,7 +289,7 @@ def compute_opposition_metrics(
     opp: dict[int, dict[str, list]] = {}
     for year in range(YEAR_MIN, YEAR_MAX + 1):
         opp[year] = {
-            "centrist_support": [],
+            "centrist_support_strict": [],
             "extremity": [],
             "passed": [],
             "n": 0,
@@ -310,7 +313,7 @@ def compute_opposition_metrics(
             if submitter_party in coal:
                 continue
 
-            opp[year]["centrist_support"].append(d["centrist_support"][idx])
+            opp[year]["centrist_support_strict"].append(d["centrist_support_strict"][idx])
             opp[year]["extremity"].append(d["extremity"][idx])
             opp[year]["passed"].append(d["passed"][idx])
             opp[year]["n"] += 1
@@ -326,14 +329,14 @@ def compute_domain_metrics(
     non_mig: dict[int, dict[str, list]] = {}
 
     for year in range(YEAR_MIN, YEAR_MAX + 1):
-        mig[year] = {"centrist_support": [], "extremity": [], "passed": [], "n": 0}
-        non_mig[year] = {"centrist_support": [], "extremity": [], "passed": [], "n": 0}
+        mig[year] = {"centrist_support_strict": [], "extremity": [], "passed": [], "n": 0}
+        non_mig[year] = {"centrist_support_strict": [], "extremity": [], "passed": [], "n": 0}
 
     for year, d in yearly_raw.items():
         for idx in range(len(d["titles"])):
             cat = d["categories"][idx]
             target = mig if cat == "asiel/vreemdelingen" else non_mig
-            target[year]["centrist_support"].append(d["centrist_support"][idx])
+            target[year]["centrist_support_strict"].append(d["centrist_support_strict"][idx])
             target[year]["extremity"].append(d["extremity"][idx])
             target[year]["passed"].append(d["passed"][idx])
             target[year]["n"] += 1
@@ -361,7 +364,7 @@ def compute_extremity_stratified(
         period = "pre-2024" if year < BREAK_YEAR else "post-2024"
         for idx in range(len(d["titles"])):
             ext = d["extremity"][idx]
-            cs = d["centrist_support"][idx]
+            cs = d["centrist_support_strict"][idx]
             if np.isnan(ext) or cs is None or (isinstance(cs, float) and np.isnan(cs)):
                 continue
             if ext < 2:
@@ -377,17 +380,33 @@ def compute_extremity_stratified(
     return pre_post
 
 
+def compute_left_support_yearly(con: duckdb.DuckDBPyConnection) -> dict[int, dict]:
+    """Query left_support_mp yearly averages from right_wing_motions."""
+    rows = con.execute("""
+        SELECT year, AVG(left_support_mp), COUNT(*)
+        FROM right_wing_motions
+        WHERE classified = TRUE AND left_support_mp IS NOT NULL
+        GROUP BY year ORDER BY year
+    """).fetchall()
+
+    result: dict[int, dict] = {}
+    for year, avg, n in rows:
+        year = int(year)
+        result[year] = {"mean_left_support": avg, "n": n}
+    return result
+
+
 def yearly_summary(yearly: dict[int, dict]) -> dict[int, dict]:
     """Compute mean values from raw lists."""
     summary: dict[int, dict] = {}
     for year, d in yearly.items():
         s: dict[str, Any] = {}
-        for key in ["centrist_support", "right_support", "left_opposition", "extremity"]:
+        for key in ["centrist_support_strict", "center_right_support", "right_support", "left_opposition", "extremity"]:
             vals = [v for v in d.get(key, []) if not (isinstance(v, float) and np.isnan(v))]
             s[f"mean_{key}"] = np.mean(vals) if vals else float("nan")
         passes = [p for p in d.get("passed", []) if p is not None]
         s["pass_rate"] = sum(passes) / len(passes) if passes else float("nan")
-        s["n"] = len(d.get("motion_ids", d.get("centrist_support", [])))
+        s["n"] = len(d.get("motion_ids", d.get("centrist_support_strict", [])))
         summary[year] = s
     return summary
 
@@ -486,16 +505,18 @@ def create_figure_1(
     colour_non_mig = "#4CAF50"
     colour_baseline = "#9E9E9E"
 
-    ax.plot(years_arr, _vals(yearly_sum, "mean_centrist_support"),
+    ax.plot(years_arr, _vals(yearly_sum, "mean_centrist_support_strict"),
             marker="o", color=colour_rw, linewidth=2, label="All right-wing", zorder=5)
-    ax.plot(years_arr, _vals(opp_sum, "mean_centrist_support"),
+    ax.plot(years_arr, _vals(opp_sum, "mean_centrist_support_strict"),
             marker="s", color=colour_opp, linewidth=1.5, linestyle="--", label="Opposition-only", zorder=4)
-    ax.plot(years_arr, _vals(mig_sum, "mean_centrist_support"),
+    ax.plot(years_arr, _vals(mig_sum, "mean_centrist_support_strict"),
             marker="^", color=colour_mig, linewidth=1.5, linestyle=":", label="Migration", zorder=3)
-    ax.plot(years_arr, _vals(non_mig_sum, "mean_centrist_support"),
+    ax.plot(years_arr, _vals(non_mig_sum, "mean_centrist_support_strict"),
             marker="v", color=colour_non_mig, linewidth=1.5, linestyle="-.", label="Non-migration", zorder=2)
     ax.plot(years_arr, _vals(baseline_sum, "mean_centrist_support"),
             color=colour_baseline, linewidth=1, linestyle="dashed", alpha=0.7, zorder=1, label="All motions (baseline)")
+    ax.plot(years_arr, _vals(yearly_sum, "mean_center_right_support"),
+            marker="D", color="#FF8F00", linewidth=1.5, linestyle="--", label="Center-right (VVD/BBB)", zorder=3)
 
     ax.axvline(x=BREAK_YEAR - 0.5, color="black", linestyle=":", alpha=0.5, linewidth=1)
     ax.annotate("2024", xy=(BREAK_YEAR - 0.3, ax.get_ylim()[1] * 0.95 if ax.get_ylim()[1] > 0 else 0.95),
@@ -506,8 +527,8 @@ def create_figure_1(
             bbox=dict(boxstyle="round", facecolor="white", alpha=0.8))
 
     ax.set_xlabel("Year")
-    ax.set_ylabel("Centrist support (fraction of parties)")
-    ax.set_title("Centrist Support for Right-Wing Motions Over Time", fontweight="bold")
+    ax.set_ylabel("Centrist support (strict — fraction of parties)")
+    ax.set_title("Centrist Support (Strict) for Right-Wing Motions Over Time", fontweight="bold")
     ax.legend(loc="lower right", fontsize=8, ncol=2)
     ax.set_ylim(0, 1.05)
     ax.grid(True, alpha=0.3)
@@ -594,10 +615,10 @@ def create_figure_2(
 
     pre_means_a = np.array(pre_means)
     post_means_a = np.array(post_means)
-    pre_lower = pre_means_a - np.array(pre_p25s)
-    pre_upper = np.array(pre_p75s) - pre_means_a
-    post_lower = post_means_a - np.array(post_p25s)
-    post_upper = np.array(post_p75s) - post_means_a
+    pre_lower = np.maximum(pre_means_a - np.array(pre_p25s), 0)
+    pre_upper = np.maximum(np.array(pre_p75s) - pre_means_a, 0)
+    post_lower = np.maximum(post_means_a - np.array(post_p25s), 0)
+    post_upper = np.maximum(np.array(post_p75s) - post_means_a, 0)
     pre_yerr = np.vstack([pre_lower, pre_upper])
     post_yerr = np.vstack([post_lower, post_upper])
 
@@ -616,7 +637,7 @@ def create_figure_2(
                  f"N={n}", ha="center", va="bottom", fontsize=8, fontweight="bold")
 
     overall_cs_mean = np.average(
-        _vals(yearly_sum, "mean_centrist_support"),
+        _vals(yearly_sum, "mean_centrist_support_strict"),
         weights=_vals(yearly_sum, "n"),
     )
     ax2.axhline(y=overall_cs_mean, color="grey", linestyle="--", alpha=0.7, linewidth=1,
@@ -638,6 +659,47 @@ def create_figure_2(
     return path
 
 
+def create_figure_3(
+    left_yearly: dict[int, dict],
+) -> str:
+    """Figure 3: Left-party support for right-wing motions (bar chart)."""
+    years = sorted(left_yearly.keys())
+    years_arr = np.array(years)
+
+    means = np.array([left_yearly[y]["mean_left_support"] for y in years])
+    ns = np.array([left_yearly[y]["n"] for y in years])
+
+    # Weighted all-years mean
+    overall_mean = np.average(means, weights=ns) if ns.sum() > 0 else 0.0
+
+    fig, ax = plt.subplots(figsize=(12, 6))
+    bars = ax.bar(years_arr, means, color="#1565C0", edgecolor="white", alpha=0.9)
+    for bar, n in zip(bars, ns):
+        ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.005,
+                f"N={int(n)}", ha="center", va="bottom", fontsize=8)
+
+    ax.axhline(y=overall_mean, color="#D32F2F", linestyle="--", alpha=0.8, linewidth=1,
+               label=f"Weighted mean ({overall_mean:.3f})")
+    ax.axvline(x=BREAK_YEAR - 0.5, color="black", linestyle=":", alpha=0.5, linewidth=1)
+    ax.annotate("2024", xy=(BREAK_YEAR - 0.3, ax.get_ylim()[1] * 0.95),
+                fontsize=9, color="black", alpha=0.7)
+
+    ax.set_xlabel("Year")
+    ax.set_ylabel("Mean left_support_mp")
+    ax.set_title("Left-wing party support for right-wing motions", fontweight="bold")
+    ax.legend(fontsize=9)
+    ax.set_xticks(years_arr)
+    ax.set_xticklabels([str(y) for y in years], rotation=45)
+    ax.grid(True, alpha=0.3, axis="y")
+
+    plt.tight_layout()
+    path = str(REPORTS_DIR / "breakpoint_figure_3.png")
+    fig.savefig(path, dpi=150, bbox_inches="tight")
+    plt.close(fig)
+    logger.info("Saved Figure 3 to %s", path)
+    return path
+
+
 def generate_report(
     yearly_sum: dict[int, dict],
     opp_sum: dict[int, dict],
@@ -647,8 +709,10 @@ def generate_report(
     ext_stratified: dict[str, dict[str, list]],
     yearly_raw: dict[int, dict],
     opp_raw: dict[int, dict],
+    left_yearly: dict[int, dict],
     fig1_path: str,
     fig2_path: str,
+    fig3_path: str,
     audit_sample: list[dict],
     audit_notes: str = "",
 ) -> str:
@@ -674,8 +738,8 @@ def generate_report(
     opp_post_ext = []
 
     for y, d in yearly_raw.items():
-        for idx in range(len(d.get("centrist_support", []))):
-            cs = d["centrist_support"][idx]
+        for idx in range(len(d.get("centrist_support_strict", []))):
+            cs = d["centrist_support_strict"][idx]
             ext = d["extremity"][idx]
             if not (isinstance(cs, float) and np.isnan(cs)):
                 if y < BREAK_YEAR:
@@ -689,8 +753,8 @@ def generate_report(
                     rw_post_ext.append(ext)
 
     for y, d in opp_raw.items():
-        for idx in range(len(d.get("centrist_support", []))):
-            cs = d["centrist_support"][idx]
+        for idx in range(len(d.get("centrist_support_strict", []))):
+            cs = d["centrist_support_strict"][idx]
             ext = d["extremity"][idx]
             if not (isinstance(cs, float) and np.isnan(cs)):
                 if y < BREAK_YEAR:
@@ -710,11 +774,11 @@ def generate_report(
     d_opp_ext = cohens_d(np.array(opp_pre_ext), np.array(opp_post_ext)) if opp_pre_ext and opp_post_ext else float("nan")
 
     # Yearly summary table
-    yearly_table = "| Year | N (RW) | Centrist Support | Extremity | Right Support | Left Opp. |\n"
-    yearly_table += "|------|--------|-----------------|-----------|---------------|----------|\n"
+    yearly_table = "| Year | N (RW) | Centrist Support (Strict) | Extremity | Right Support | Left Opp. |\n"
+    yearly_table += "|------|--------|---------------------------|-----------|---------------|----------|\n"
     for y in years:
         n = _val(yearly_sum, y, "n")
-        cs = _val(yearly_sum, y, "mean_centrist_support")
+        cs = _val(yearly_sum, y, "mean_centrist_support_strict")
         ext = _val(yearly_sum, y, "mean_extremity")
         rs = _val(yearly_sum, y, "mean_right_support")
         lo = _val(yearly_sum, y, "mean_left_opposition")
@@ -817,8 +881,8 @@ def generate_report(
     ]
 
     for domain_name, domain_sum in [("Migration", mig_sum), ("Non-migration", non_mig_sum)]:
-        pre_cs = np.nanmean([_val(domain_sum, y, "mean_centrist_support") for y in pre_years])
-        post_cs = np.nanmean([_val(domain_sum, y, "mean_centrist_support") for y in post_years])
+        pre_cs = np.nanmean([_val(domain_sum, y, "mean_centrist_support_strict") for y in pre_years])
+        post_cs = np.nanmean([_val(domain_sum, y, "mean_centrist_support_strict") for y in post_years])
         lines.append(
             f"| {domain_name} | {pre_cs:.3f} | {post_cs:.3f} | {post_cs - pre_cs:+.3f} |"
         )
@@ -835,19 +899,55 @@ def generate_report(
         "If centrist support rose uniformly across all buckets, the shift is about volume",
         "(more motions) rather than tolerance. If only the 1-2 bucket rose, right-wing",
         "parties filed milder motions post-2024 and the 'shift' is illusory.",
+    ]
+
+    # Section 6: Left support for right-wing motions
+    left_years_sorted = sorted(left_yearly.keys())
+    left_pre_years_list = [y for y in pre_years if y in left_yearly]
+    left_post_years_list = [y for y in post_years if y in left_yearly]
+
+    left_pre_vals = [left_yearly[y]["mean_left_support"] for y in left_pre_years_list]
+    left_post_vals = [left_yearly[y]["mean_left_support"] for y in left_post_years_list]
+    left_pre_mean = np.mean(left_pre_vals) if left_pre_vals else float("nan")
+    left_post_mean = np.mean(left_post_vals) if left_post_vals else float("nan")
+    left_delta = left_post_mean - left_pre_mean
+
+    left_table = "| Year | N | Mean left_support_mp |\n"
+    left_table += "|------|---|---------------------|\n"
+    for y in left_years_sorted:
+        ls = left_yearly[y]["mean_left_support"]
+        n = left_yearly[y]["n"]
+        left_table += f"| {y} | {int(n)} | {ls:.4f} |\n"
+
+    lines += [
+        "",
+        "## 6. Left-wing support for right-wing motions",
+        "",
+        left_table,
+        "",
+        f"| Metric | Pre-2024 Mean | Post-2024 Mean | Δ |",
+        f"|--------|--------------|---------------|-----|",
+        f"| Left Support (MP) | {left_pre_mean:.4f} | {left_post_mean:.4f} | {left_delta:+.4f} |",
         "",
-        "## 6. Manual Extremity Audit",
+        f"**Interpretation:** Left parties moved from {left_pre_mean:.1%} to {left_post_mean:.1%} "
+        f"support — a {abs(left_delta):.1f} point shift. "
+        "Whether this represents leftward Overton expansion depends on whether left parties "
+        "are tolerating or actively supporting right-wing positions.",
+        "",
+        f"![Figure 3: Left-wing party support for right-wing motions]({Path(fig3_path).name})",
+        "",
+        "## 7. Manual Extremity Audit",
         "",
         audit_notes,
         "",
         audit_table,
         "",
-        "## 7. Limitations",
+        "## 8. Limitations",
         "",
         "- **Small-N time series:** 8 pre-2024 years and at most 3 post-2024 years (2026 is partial).",
         "   Effect sizes are descriptive, not confirmatory.",
         "- **LLM extremity scores:** Content-based, not independently validated beyond the",
-        "   manual audit above. See §6 for agreement rate and noted biases.",
+        "   manual audit above. See §7 for agreement rate and noted biases.",
         "- **Coalition composition:** Hardcoded per year. 2024 is ambiguous (Rutte IV until July,",
         "   Schoof thereafter). Early 2024 motions may be miscoded as Schoof-era.",
         "- **Submitter party identification:** Parsed from motion title prefixes (e.g.,",
@@ -856,12 +956,13 @@ def generate_report(
         "- **Keyword penetration not analyzed:** The right-wing keyword set was derived",
         "   differentially from right-wing motions, making it circular for adoption analysis.",
         "",
-        "## 8. Figures",
+        "## 9. Figures",
         "",
         f"![Figure 1: Centrist Support Over Time]({Path(fig1_path).name})",
         f"![Figure 2: Extremity Trends and Stratified Centrist Support]({Path(fig2_path).name})",
+        f"![Figure 3: Left-wing party support for right-wing motions]({Path(fig3_path).name})",
         "",
-        "## 9. Conclusion",
+        "## 10. Conclusion",
         "",
         "*(Fill in after reviewing all indicators and audit results.)*",
     ]
@@ -895,6 +996,9 @@ def main() -> int:
     logger.info("Computing extremity-stratified pass rates...")
     ext_stratified = compute_extremity_stratified(yearly_raw)
 
+    logger.info("Computing left-support yearly averages...")
+    left_yearly = compute_left_support_yearly(con)
+
     con.close()
 
     yearly_sum = yearly_summary(yearly_raw)
@@ -909,6 +1013,9 @@ def main() -> int:
     logger.info("Generating Figure 2...")
     fig2_path = create_figure_2(yearly_sum, opp_sum, mig_sum, non_mig_sum, ext_stratified)
 
+    logger.info("Generating Figure 3...")
+    fig3_path = create_figure_3(left_yearly)
+
     logger.info("Sampling motions for manual audit...")
     audit_sample = sample_audit(yearly_raw)
     print_audit(audit_sample)
@@ -931,8 +1038,10 @@ def main() -> int:
         ext_stratified=ext_stratified,
         yearly_raw=yearly_raw,
         opp_raw=opp_raw,
+        left_yearly=left_yearly,
         fig1_path=fig1_path,
         fig2_path=fig2_path,
+        fig3_path=fig3_path,
         audit_sample=audit_sample,
         audit_notes=audit_notes,
     )
@@ -940,6 +1049,7 @@ def main() -> int:
     print(f"\nReport: {report_path}")
     print(f"Figure 1: {fig1_path}")
     print(f"Figure 2: {fig2_path}")
+    print(f"Figure 3: {fig3_path}")
     return 0
 
 

reports/overton_window/breakpoint_analysis.md

@@ -12,19 +12,19 @@ and content extremity for right-wing motions in the Tweede Kamer.
 
 ## 1. Yearly Aggregate Metrics (All Right-Wing Motions)
 
-| Year | N (RW) | Centrist Support | Extremity | Right Support | Left Opp. |
-|------|--------|-----------------|-----------|---------------|----------|
-| 2016 | 6 | 0.722 | 2.00 | 1.000 | 0.708 |
+| Year | N (RW) | Centrist Support (Strict) | Extremity | Right Support | Left Opp. |
+|------|--------|---------------------------|-----------|---------------|----------|
+| 2016 | 6 | 0.667 | 2.00 | 1.000 | 0.708 |
 | 2017 | 0 | N/A | N/A | N/A | N/A |
 | 2018 | 5 | 1.000 | 1.40 | 0.800 | 0.480 |
-| 2019 | 195 | 0.410 | 2.14 | 0.838 | 0.746 |
-| 2020 | 469 | 0.326 | 2.26 | 0.818 | 0.758 |
-| 2021 | 425 | 0.339 | 2.24 | 0.903 | 0.788 |
-| 2022 | 446 | 0.404 | 2.16 | 0.891 | 0.820 |
-| 2023 | 365 | 0.457 | 2.24 | 0.900 | 0.821 |
-| 2024 | 469 | 0.670 | 1.99 | 0.885 | 0.756 |
-| 2025 | 455 | 0.597 | 2.25 | 0.895 | 0.799 |
-| 2026 | 151 | 0.518 | 2.33 | 0.916 | 0.834 |
+| 2019 | 195 | 0.380 | 2.14 | 0.838 | 0.746 |
+| 2020 | 469 | 0.300 | 2.26 | 0.818 | 0.758 |
+| 2021 | 425 | 0.175 | 2.24 | 0.903 | 0.788 |
+| 2022 | 446 | 0.201 | 2.16 | 0.891 | 0.820 |
+| 2023 | 365 | 0.255 | 2.24 | 0.900 | 0.821 |
+| 2024 | 469 | 0.595 | 1.99 | 0.885 | 0.756 |
+| 2025 | 455 | 0.474 | 2.25 | 0.895 | 0.799 |
+| 2026 | 151 | 0.334 | 2.33 | 0.916 | 0.834 |
 
 
 ## 2. Pre/Post 2024 Comparison
@@ -35,7 +35,7 @@ and content extremity for right-wing motions in the Tweede Kamer.
 
 | Metric | Pre-2024 Mean | Post-2024 Mean | Δ | Cohen's d |
 |--------|--------------|---------------|-----|-----------|
-| Centrist Support | 0.384 | 0.618 | +0.234 | +0.68 |
+| Centrist Support | 0.251 | 0.507 | +0.256 | +0.65 |
 | Extremity | 2.21 | 2.15 | -0.07 | -0.09 |
 
 **Interpretation:** Cohen's d values quantify effect sizes (|d| < 0.2 small, 0.5 medium, > 0.8 large).
@@ -45,7 +45,7 @@ These are descriptive, not inferential — with only 8 pre-2024 years and 3 post
 
 | Metric | Pre-2024 Mean | Post-2024 Mean | Δ | Cohen's d | N pre / N post |
 |--------|--------------|---------------|-----|-----------|---------------|
-| Centrist Support | 0.270 | 0.543 | +0.272 | +0.85 | 1295 / 405 |
+| Centrist Support | 0.130 | 0.437 | +0.307 | +0.88 | 1295 / 405 |
 | Extremity | 2.28 | 2.18 | -0.10 | -0.14 | 1295 / 405 |
 
 **Interpretation gate:** If opposition metrics also rise post-2024, the shift is not
@@ -67,21 +67,21 @@ Migration = category `asiel/vreemdelingen`. Non-migration = all other categories
 
 | Domain | Pre-2024 Mean CS | Post-2024 Mean CS | Δ CS |
 |--------|-----------------|------------------|------|
-| Migration | 0.303 | 0.536 | +0.233 |
-| Non-migration | 0.529 | 0.605 | +0.076 |
+| Migration | 0.146 | 0.361 | +0.215 |
+| Non-migration | 0.435 | 0.487 | +0.052 |
 
 ## 5. Extremity-Stratified Centrist Support
 
 | Bucket | Period | N | Mean CS | Median CS | P25 | P75 |
 |--------|--------|---|---------|-----------|---|-----|
-| 1-2 (mild) | Pre-2024 | 221 | 0.522 | 0.400 | 0.250 | 1.000 |
-| | Post-2024 | 181 | 0.775 | 1.000 | 0.600 | 1.000 |
-| 2-3 (moderate) | Pre-2024 | 1205 | 0.403 | 0.250 | 0.000 | 0.750 |
-| | Post-2024 | 640 | 0.606 | 0.600 | 0.250 | 1.000 |
-| 3-4 (high) | Pre-2024 | 352 | 0.295 | 0.250 | 0.000 | 0.500 |
-| | Post-2024 | 175 | 0.565 | 0.600 | 0.250 | 0.800 |
-| 4-5 (extreme) | Pre-2024 | 133 | 0.212 | 0.250 | 0.000 | 0.250 |
-| | Post-2024 | 79 | 0.474 | 0.500 | 0.250 | 0.800 |
+| 1-2 (mild) | Pre-2024 | 221 | 0.422 | 0.500 | 0.000 | 1.000 |
+| | Post-2024 | 181 | 0.728 | 1.000 | 0.667 | 1.000 |
+| 2-3 (moderate) | Pre-2024 | 1205 | 0.267 | 0.000 | 0.000 | 0.500 |
+| | Post-2024 | 640 | 0.497 | 0.500 | 0.000 | 1.000 |
+| 3-4 (high) | Pre-2024 | 352 | 0.150 | 0.000 | 0.000 | 0.000 |
+| | Post-2024 | 175 | 0.419 | 0.333 | 0.000 | 0.667 |
+| 4-5 (extreme) | Pre-2024 | 133 | 0.091 | 0.000 | 0.000 | 0.000 |
+| | Post-2024 | 79 | 0.275 | 0.000 | 0.000 | 0.667 |
 
 
 **Key test:** If centrist support for high-extremity motions (3-5) rose
@@ -91,7 +91,31 @@ If centrist support rose uniformly across all buckets, the shift is about volume
 (more motions) rather than tolerance. If only the 1-2 bucket rose, right-wing
 parties filed milder motions post-2024 and the 'shift' is illusory.
 
-## 6. Manual Extremity Audit
+## 6. Left-wing support for right-wing motions
+
+| Year | N | Mean left_support_mp |
+|------|---|---------------------|
+| 2016 | 6 | 0.2917 |
+| 2018 | 5 | 0.5200 |
+| 2019 | 195 | 0.2531 |
+| 2020 | 469 | 0.2414 |
+| 2021 | 425 | 0.2113 |
+| 2022 | 446 | 0.1807 |
+| 2023 | 365 | 0.1779 |
+| 2024 | 469 | 0.2441 |
+| 2025 | 455 | 0.2015 |
+| 2026 | 151 | 0.1594 |
+
+
+| Metric | Pre-2024 Mean | Post-2024 Mean | Δ |
+|--------|--------------|---------------|-----|
+| Left Support (MP) | 0.2680 | 0.2017 | -0.0663 |
+
+**Interpretation:** Left parties moved from 26.8% to 20.2% support — a 0.1 point shift. Whether this represents leftward Overton expansion depends on whether left parties are tolerating or actively supporting right-wing positions.
+
+![Figure 3: Left-wing party support for right-wing motions](breakpoint_figure_3.png)
+
+## 7. Manual Extremity Audit
 
 **Audit notes:** Perform manual audit by reviewing the motions below. Record agreement per motion. Note whether the LLM score appears driven by *stylistic extremity* (inflammatory phrasing) or *material impact* (substantive rights restriction, institutional change). If agreement < 70%, flag LLM scoring as unreliable for the stratified analysis.
 
@@ -119,12 +143,12 @@ parties filed milder motions post-2024 and the 'shift' is illusory.
 | 20 | 2019 | sociaal/jeugd | 4 | 4-5 (extreme) | | |
 
 
-## 7. Limitations
+## 8. Limitations
 
 - **Small-N time series:** 8 pre-2024 years and at most 3 post-2024 years (2026 is partial).
    Effect sizes are descriptive, not confirmatory.
 - **LLM extremity scores:** Content-based, not independently validated beyond the
-   manual audit above. See §6 for agreement rate and noted biases.
+   manual audit above. See §7 for agreement rate and noted biases.
 - **Coalition composition:** Hardcoded per year. 2024 is ambiguous (Rutte IV until July,
    Schoof thereafter). Early 2024 motions may be miscoded as Schoof-era.
 - **Submitter party identification:** Parsed from motion title prefixes (e.g.,
@@ -133,11 +157,12 @@ parties filed milder motions post-2024 and the 'shift' is illusory.
 - **Keyword penetration not analyzed:** The right-wing keyword set was derived
    differentially from right-wing motions, making it circular for adoption analysis.
 
-## 8. Figures
+## 9. Figures
 
 ![Figure 1: Centrist Support Over Time](breakpoint_figure_1.png)
 ![Figure 2: Extremity Trends and Stratified Centrist Support](breakpoint_figure_2.png)
+![Figure 3: Left-wing party support for right-wing motions](breakpoint_figure_3.png)
 
-## 9. Conclusion
+## 10. Conclusion
 
 *(Fill in after reviewing all indicators and audit results.)*