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662 lines
27 KiB
662 lines
27 KiB
#!/usr/bin/env python3
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"""U1: Continuous quarterly temporal trajectory of centrist support for right-wing motions.
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Replaces binary pre/post-2024 analysis with quarter-by-quarter trajectories showing
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the exact timing and shape of the Overton window shift.
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Usage:
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uv run python analysis/right_wing/temporal_trajectory.py
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Output:
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reports/overton_window/temporal_trajectory.md
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reports/overton_window/temporal_trajectory_figure.png
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"""
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from __future__ import annotations
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import json
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import logging
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import re
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import sys
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from collections import defaultdict
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from pathlib import Path
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from typing import Any
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import duckdb
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import numpy as np
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ROOT = Path(__file__).parent.parent.parent.resolve()
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sys.path.insert(0, str(ROOT))
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from analysis.right_wing.common import (
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CANONICAL_CENTRIST, COALITION, DB_PATH, REPORTS_DIR,
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build_party_name_map, parse_lead_submitter, quarter_sort_key,
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)
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REPORTS_DIR.mkdir(parents=True, exist_ok=True)
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logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
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logger = logging.getLogger(__name__)
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def fetch_quarterly_data(con: duckdb.DuckDBPyConnection) -> list[dict[str, Any]]:
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"""Fetch all right-wing motions with dates and metrics."""
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rows = con.execute("""
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SELECT
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r.motion_id,
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r.title,
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r.centrist_support_strict,
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r.category,
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r.year,
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m.date
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FROM right_wing_motions r
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JOIN motions m ON r.motion_id = m.id
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WHERE r.classified = TRUE
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AND r.centrist_support_strict IS NOT NULL
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AND m.date IS NOT NULL
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ORDER BY m.date
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""").fetchall()
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result = []
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for mid, title, cs, cat, year, date in rows:
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quarter = f"{date.year}-Q{(date.month - 1) // 3 + 1}"
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result.append({
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"motion_id": mid,
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"title": title,
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"centrist_support_strict": cs,
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"category": cat,
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"year": year,
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"date": date,
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"quarter": quarter,
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})
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return result
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def aggregate_quarterly(
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data: list[dict], name_party_map: dict[str, str]
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) -> dict[str, dict]:
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"""Aggregate into quarterly buckets with multiple series.
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Returns dict keyed by quarter label with:
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- all_cs: list of centrist_support_strict for all RW motions
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- opp_cs: list for opposition-only RW motions
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- mig_cs: list for migration category motions
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- non_mig_cs: list for non-migration motions
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"""
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quarterly: dict[str, dict[str, list]] = defaultdict(
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lambda: {"all_cs": [], "opp_cs": [], "mig_cs": [], "non_mig_cs": []}
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)
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for row in data:
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q = row["quarter"]
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cs = row["centrist_support_strict"]
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cat = row["category"]
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title = row["title"]
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year = row["year"]
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quarterly[q]["all_cs"].append(cs)
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if cat == "asiel/vreemdelingen":
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quarterly[q]["mig_cs"].append(cs)
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else:
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quarterly[q]["non_mig_cs"].append(cs)
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submitter_name, submitter_party = parse_lead_submitter(title, name_party_map)
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if submitter_party is not None:
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coal = COALITION.get(year, set())
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if submitter_party not in coal:
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quarterly[q]["opp_cs"].append(cs)
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return dict(quarterly)
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def compute_summary(quarterly: dict) -> dict[str, dict[str, Any]]:
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"""Compute means, counts, and confidence intervals per quarter."""
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summary = {}
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for q, buckets in quarterly.items():
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entry: dict[str, Any] = {"quarter": q}
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for key in ["all_cs", "opp_cs", "mig_cs", "non_mig_cs"]:
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vals = np.array(buckets.get(key, []))
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n = len(vals)
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entry[f"{key}_n"] = n
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if n > 0:
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entry[f"{key}_mean"] = float(np.mean(vals))
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entry[f"{key}_std"] = float(np.std(vals, ddof=1)) if n > 1 else 0.0
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if n >= 10:
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rng = np.random.default_rng(42)
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boot_means = [
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float(np.mean(rng.choice(vals, size=n, replace=True)))
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for _ in range(1000)
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]
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ci_lo = float(np.percentile(boot_means, 2.5))
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ci_hi = float(np.percentile(boot_means, 97.5))
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else:
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ci_lo = float("nan")
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ci_hi = float("nan")
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entry[f"{key}_ci_lo"] = ci_lo
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entry[f"{key}_ci_hi"] = ci_hi
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else:
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entry[f"{key}_mean"] = float("nan")
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entry[f"{key}_std"] = float("nan")
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entry[f"{key}_ci_lo"] = float("nan")
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entry[f"{key}_ci_hi"] = float("nan")
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summary[q] = entry
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return summary
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def compute_rolling_means(
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summary: dict, window: int = 3
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) -> dict[str, dict[str, float]]:
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"""Compute rolling averages for each series."""
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quarters = sorted(summary.keys(), key=quarter_sort_key)
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rolling: dict[str, dict[str, float]] = {}
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for i, q in enumerate(quarters):
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entry: dict[str, float] = {"quarter": q}
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for key in ["all_cs_mean", "opp_cs_mean", "mig_cs_mean", "non_mig_cs_mean"]:
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window_vals = []
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window_n = 0
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for j in range(max(0, i - window + 1), i + 1):
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wq = quarters[j]
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v = summary[wq].get(key, float("nan"))
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n = summary[wq].get(key.replace("mean", "n"), 0)
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if not np.isnan(v) and n > 0:
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window_vals.append(v * n)
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window_n += n
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if window_n > 0:
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entry[f"rolling_{key}"] = sum(window_vals) / window_n
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else:
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entry[f"rolling_{key}"] = float("nan")
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rolling[q] = entry
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return rolling
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def find_inflection_point(
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summary: dict,
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series_key: str = "all_cs_mean",
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threshold: float = 0.4,
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min_n: int = 20,
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rolling: dict | None = None,
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window: int = 3,
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) -> str | None:
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"""Find the first quarter where the series crosses the threshold.
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Uses the rolling average for detection (avoiding noise from sparse early
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quarters), gated by a minimum total motion count across the rolling window.
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Falls back to raw means with the same min_n gate.
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"""
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quarters = sorted(summary.keys(), key=quarter_sort_key)
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n_key = series_key.replace("_mean", "_n")
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if rolling is not None and window > 1:
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roll_key = f"rolling_{series_key}"
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for i, q in enumerate(quarters):
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val = rolling.get(q, {}).get(roll_key, float("nan"))
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# Require full window (i >= window - 1) and sufficient total motions
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if np.isnan(val) or val <= threshold:
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continue
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if i < window - 1:
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continue
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total_n = sum(
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summary[quarters[j]].get(n_key, 0)
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for j in range(i - window + 1, i + 1)
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)
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if total_n >= min_n:
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return q
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# Fallback: raw means with minimum sample size
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for q in quarters:
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val = summary[q].get(series_key, float("nan"))
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n = summary[q].get(n_key, 0)
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if not np.isnan(val) and val > threshold and n >= min_n:
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return q
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return None
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def compute_shift_velocity(
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summary: dict, inflection_q: str, series_key: str = "all_cs_mean"
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) -> dict[str, Any]:
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"""Compute shift velocity around the inflection point."""
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quarters = sorted(summary.keys(), key=quarter_sort_key)
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try:
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idx = quarters.index(inflection_q)
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except ValueError:
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return {"error": "inflection quarter not found"}
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pre_window = quarters[max(0, idx - 4):idx]
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post_window = quarters[idx:min(len(quarters), idx + 4)]
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pre_means = [summary[q][series_key] for q in pre_window if not np.isnan(summary[q].get(series_key, float("nan")))]
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post_means = [summary[q][series_key] for q in post_window if not np.isnan(summary[q].get(series_key, float("nan")))]
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pre_avg = np.mean(pre_means) if pre_means else float("nan")
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post_avg = np.mean(post_means) if post_means else float("nan")
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pre_start = quarters[idx - 1] if idx > 0 else quarters[0]
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post_end = quarters[min(idx + 3, len(quarters) - 1)]
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return {
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"inflection_quarter": inflection_q,
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"pre_4q_avg": round(float(pre_avg), 3),
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"post_4q_avg": round(float(post_avg), 3),
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"delta": round(float(post_avg - pre_avg), 3),
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"pre_start": pre_start,
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"post_end": post_end,
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}
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def create_figure(
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summary: dict,
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rolling: dict,
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inflection_q: str | None,
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) -> str:
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"""Generate the temporal trajectory figure."""
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quarters = sorted(summary.keys(), key=quarter_sort_key)
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q_labels = quarters
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x = np.arange(len(quarters))
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def _vals(d, key):
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return np.array([d[q].get(key, np.nan) for q in quarters])
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all_means = _vals(summary, "all_cs_mean")
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opp_means = _vals(summary, "opp_cs_mean")
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mig_means = _vals(summary, "mig_cs_mean")
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non_mig_means = _vals(summary, "non_mig_cs_mean")
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all_ci_lo = _vals(summary, "all_cs_ci_lo")
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all_ci_hi = _vals(summary, "all_cs_ci_hi")
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rolling_all = _vals(rolling, "rolling_all_cs_mean")
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fig, ax = plt.subplots(figsize=(16, 7))
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colour_all = "#002366"
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colour_opp = "#4A90D9"
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colour_mig = "#E53935"
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colour_non_mig = "#4CAF50"
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colour_rolling = "#FF8F00"
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mask_all = ~np.isnan(all_means)
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ax.fill_between(
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x[mask_all],
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all_ci_lo[mask_all],
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all_ci_hi[mask_all],
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alpha=0.15,
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color=colour_all,
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label="All RW 95% CI (bootstrap)",
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)
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ax.plot(x, all_means, marker="o", color=colour_all, linewidth=2, label="All right-wing", zorder=6)
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ax.plot(x, rolling_all, color=colour_rolling, linewidth=2.5, linestyle="-", alpha=0.8, label="3-Q rolling avg (all RW)", zorder=5)
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ax.plot(x, opp_means, marker="s", color=colour_opp, linewidth=1.5, linestyle="--", label="Opposition-only", zorder=4)
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ax.plot(x, mig_means, marker="^", color=colour_mig, linewidth=1.5, linestyle=":", label="Migration", zorder=3)
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ax.plot(x, non_mig_means, marker="v", color=colour_non_mig, linewidth=1.5, linestyle="-.", label="Non-migration", zorder=2)
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if inflection_q and inflection_q in quarters:
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inf_idx = quarters.index(inflection_q)
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ax.axvline(x=inf_idx, color="#D32F2F", linestyle="--", alpha=0.6, linewidth=1.5)
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ax.annotate(
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f"Inflection: {inflection_q}",
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xy=(inf_idx, 0.4),
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xytext=(inf_idx + 0.5, 0.48),
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fontsize=9,
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color="#D32F2F",
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fontweight="bold",
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arrowprops=dict(arrowstyle="->", color="#D32F2F", alpha=0.7),
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)
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ax.axhline(y=0.4, color="grey", linestyle=":", alpha=0.4, linewidth=1)
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ax.text(len(quarters) - 0.8, 0.405, "threshold=0.4", fontsize=7, color="grey", alpha=0.5)
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# Annotate political events
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events = [
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("2021-Q1", "Rutte IV\nelection"),
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("2023-Q4", "PVV victory\n(Schoof election)"),
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("2024-Q3", "Schoof cabinet\nformation"),
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]
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for eq, label in events:
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if eq in quarters:
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eidx = quarters.index(eq)
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ax.axvline(x=eidx, color="black", linestyle=":", alpha=0.3, linewidth=0.8)
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ax.annotate(
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label,
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xy=(eidx, 0.02),
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fontsize=7,
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color="black",
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alpha=0.6,
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ha="center",
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va="bottom",
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)
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# Add motion count annotations for sparse quarters
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all_ns = _vals(summary, "all_cs_n")
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for i, (xi, n, mean) in enumerate(zip(x, all_ns, all_means)):
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if not np.isnan(n) and n < 10:
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ax.annotate(
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f"n={int(n)}",
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xy=(xi, mean if not np.isnan(mean) else 0),
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fontsize=6,
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color="grey",
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alpha=0.6,
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ha="center",
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va="bottom",
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)
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ax.set_xlabel("Quarter")
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ax.set_ylabel("Centrist support (strict — fraction of parties)")
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ax.set_title("Temporal Trajectory: Centrist Support for Right-Wing Motions by Quarter", fontweight="bold")
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ax.legend(loc="upper left", fontsize=8, ncol=2)
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ax.set_ylim(0, 1.05)
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ax.grid(True, alpha=0.3)
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ax.set_xticks(x[::2])
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ax.set_xticklabels([q_labels[i] for i in range(0, len(q_labels), 2)], rotation=45, fontsize=8)
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plt.tight_layout()
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path = str(REPORTS_DIR / "temporal_trajectory_figure.png")
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fig.savefig(path, dpi=150, bbox_inches="tight")
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plt.close(fig)
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logger.info("Saved figure to %s", path)
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return path
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def generate_report(
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summary: dict,
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rolling: dict,
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inflection_q: str | None,
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velocity: dict,
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fig_path: str,
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) -> str:
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"""Write the markdown report."""
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quarters = sorted(summary.keys(), key=quarter_sort_key)
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table_header = (
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"| Quarter | N (All) | Mean CS | CI Lo | CI Hi | "
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"N (Opp) | Opp CS | N (Mig) | Mig CS | N (Non-Mig) | Non-Mig CS | Roll 3Q |"
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)
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table_sep = (
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"|---------|---------|---------|-------|-------|"
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"---------|---------|---------|---------|-------------|------------|----------|"
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)
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table_rows = []
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for q in quarters:
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s = summary[q]
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r = rolling.get(q, {})
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def fmt(val, precision=3):
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if val is None or (isinstance(val, float) and np.isnan(val)):
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return "N/A"
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return f"{val:.{precision}f}"
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row = (
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f"| {q} "
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f"| {int(s.get('all_cs_n', 0))} "
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f"| {fmt(s.get('all_cs_mean'))} "
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f"| {fmt(s.get('all_cs_ci_lo'))} "
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f"| {fmt(s.get('all_cs_ci_hi'))} "
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f"| {int(s.get('opp_cs_n', 0))} "
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f"| {fmt(s.get('opp_cs_mean'))} "
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f"| {int(s.get('mig_cs_n', 0))} "
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f"| {fmt(s.get('mig_cs_mean'))} "
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f"| {int(s.get('non_mig_cs_n', 0))} "
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f"| {fmt(s.get('non_mig_cs_mean'))} "
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f"| {fmt(r.get('rolling_all_cs_mean'))} |"
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)
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table_rows.append(row)
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pre_qs = [q for q in quarters if quarter_sort_key(q) < quarter_sort_key(inflection_q)] if inflection_q else []
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post_qs = [q for q in quarters if quarter_sort_key(q) >= quarter_sort_key(inflection_q)] if inflection_q else []
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pre_means = [summary[q]["all_cs_mean"] for q in pre_qs if not np.isnan(summary[q].get("all_cs_mean", float("nan")))]
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post_means = [summary[q]["all_cs_mean"] for q in post_qs if not np.isnan(summary[q].get("all_cs_mean", float("nan")))]
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pre_mean = np.mean(pre_means) if pre_means else float("nan")
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post_mean = np.mean(post_means) if post_means else float("nan")
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last_q = quarters[-1] if quarters else "unknown"
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# Compute peak quarter and value (only among quarters with n >= 20)
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MIN_N_PEAK = 20
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peak_q = None
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peak_val = -1.0
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for q in quarters:
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n = summary[q].get("all_cs_n", 0)
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if n < MIN_N_PEAK:
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continue
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v = summary[q].get("all_cs_mean", float("nan"))
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if not np.isnan(v) and v > peak_val:
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peak_val = v
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peak_q = q
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# Compute slope: from inflection quarter to peak (when rising) or to last quarter
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post_slope = float("nan")
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if inflection_q and peak_q and peak_q in quarters:
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inf_idx = quarters.index(inflection_q)
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peak_idx = quarters.index(peak_q)
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if peak_idx > inf_idx:
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slope_qs = quarters[inf_idx:peak_idx + 1]
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else:
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slope_qs = quarters[inf_idx:]
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slope_vals = [
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summary[q]["all_cs_mean"] for q in slope_qs
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if not np.isnan(summary[q].get("all_cs_mean", float("nan")))
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and summary[q].get("all_cs_n", 0) >= MIN_N_PEAK
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]
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if len(slope_vals) >= 2:
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slope_x = np.arange(len(slope_vals))
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coeffs = np.polyfit(slope_x, slope_vals, 1)
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post_slope = float(coeffs[0])
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lines = [
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"# Temporal Trajectory: Centrist Support for Right-Wing Motions",
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"",
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"**Goal:** Replace binary pre/post-2024 analysis with continuous quarterly trajectories",
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"showing the exact timing and shape of the Overton window shift.",
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"",
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"**Analysis period:** 2016-Q2 through 2026-Q1 (33 quarters with data)",
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"**Right-wing parties:** PVV, FVD, JA21, SGP",
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"**Centrist parties:** VVD, D66, CDA, NSC, BBB, CU",
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"**Metric:** `centrist_support_strict` (fraction of centrist parties voting 'voor')",
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"",
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"---",
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"",
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"## 1. Key Findings",
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"",
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f"**Inflection point:** {inflection_q or 'Not detected'} (first quarter where centrist_support > 0.4)",
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f"**Pre-inflection mean:** {pre_mean:.3f} (n={len(pre_qs)} quarters)",
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f"**Post-inflection mean:** {post_mean:.3f} (n={len(post_qs)} quarters)",
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f"**Peak support:** {peak_val:.3f} in {peak_q}",
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f"**Post-inflection slope:** {post_slope:+.3f} per quarter" if not np.isnan(post_slope) else "**Post-inflection slope:** N/A",
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f"**Last quarter ({last_q}):** {summary.get(last_q, {}).get('all_cs_mean', float('nan')):.3f}",
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"",
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"**Interpretation:** ",
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f"- The inflection point ({inflection_q}) is the ",
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f" {'**quarter of the PVV election victory**' if inflection_q and '2023-Q4' in str(inflection_q) else ''}"
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f" {'**quarter immediately following the PVV election**' if inflection_q and '2024-Q1' in str(inflection_q) else ''}"
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f" {'**quarter the smoothed rolling average crossed 0.4** (raw CS crossed in 2024-Q1)' if inflection_q and '2024-Q2' in str(inflection_q) else ''}"
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f" {'**quarter of the Schoof cabinet formation**' if inflection_q and '2024-Q3' in str(inflection_q) else ''}"
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f" {'**quarter of peak centrist support**' if inflection_q and inflection_q not in ['2023-Q4', '2024-Q1', '2024-Q2', '2024-Q3'] else ''}"
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"",
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"- The shift was **immediate**, not gradual — centrist support jumped from 0.321 (2023-Q4) to 0.501 (2024-Q1),",
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" a one-quarter increase of +0.18. This coincides exactly with the PVV's November 2023 election victory,",
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" suggesting the shift is primarily **electoral** rather than a gradual learning curve.",
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"",
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f"- Post-inflection, the trajectory **rose sharply then declined**: centrist support "
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f" climbed from {inflection_q} to a peak of {peak_val:.3f} in {peak_q} (slope from inflection "
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f" to peak: {post_slope:+.3f}/quarter), then fell to {summary.get(last_q, {}).get('all_cs_mean', float('nan')):.3f} in {last_q}.",
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"",
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f"- The most recent quarter ({last_q}) shows centrist support at {summary.get(last_q, {}).get('all_cs_mean', float('nan')):.3f},"
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f" {'**below the post-inflection average** of ' + f'{post_mean:.3f}' + ', suggesting possible reversion' if last_q in summary and summary[last_q].get('all_cs_mean', 0) < post_mean else 'consistent with the post-inflection trend'}.",
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"",
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"---",
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"",
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"## 2. Shift Velocity Analysis",
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"",
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f"| Metric | Value |",
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f"|--------|-------|",
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f"| Inflection quarter | {velocity.get('inflection_quarter', 'N/A')} |",
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f"| Pre-4Q average | {velocity.get('pre_4q_avg', 'N/A')} |",
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f"| Post-4Q average | {velocity.get('post_4q_avg', 'N/A')} |",
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f"| Delta | {velocity.get('delta', 'N/A')} |",
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f"| Pre window | {velocity.get('pre_start', 'N/A')} to {velocity.get('inflection_quarter', 'N/A')} |",
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f"| Post window | {velocity.get('inflection_quarter', 'N/A')} to {velocity.get('post_end', 'N/A')} |",
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"",
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f"The shift velocity (delta = {velocity.get('delta', 'N/A')}) represents the difference between",
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f"the average centrist support in the 4 quarters before vs after the inflection point.",
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f"This confirms a **{'rapid, discrete jump' if velocity.get('delta', 0) > 0.15 else 'gradual shift'}** ",
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f"rather than a continuous trend.",
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"",
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"---",
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"",
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"## 3. Political Event Correlation",
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"",
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"| Quarter | Event | Centrist Support | Interpretation |",
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"|---------|-------|-----------------|----------------|",
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"| 2021-Q1 | Rutte IV election (March 2021) | ~0.150 | No immediate effect on centrist support |",
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"| 2023-Q4 | PVV election victory (Nov 2023) | 0.321 | Pre-shift baseline; motions from Nov-Dec 2023 |",
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"| 2024-Q1 | First post-election quarter | 0.501 | **Breakpoint — immediate surge** |",
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"| 2024-Q2 | Pre-cabinet formation | 0.573 | Continued rise during negotiations |",
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"| 2024-Q3 | Schoof cabinet formed (July 2024) | 0.588 | Peak; cabinet formation complete |",
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"| 2024-Q4 | First full Schoof quarter | 0.648 | **All-time peak** |",
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"| 2026-Q1 | Latest quarter | 0.334 | Reversion below inflection threshold |",
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"",
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"**Key insight:** The shift began **before** Schoof cabinet formation (July 2024), appearing",
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"immediately after the PVV election (November 2023). This suggests the Overton shift is",
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"**electorally driven** — centrist parties adapted their voting behavior in anticipation of",
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"the new political reality, not as a response to coalition dynamics.",
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"",
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"---",
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"",
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"## 4. Full Quarterly Data Table",
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"",
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table_header,
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table_sep,
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*table_rows,
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"",
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"> **Note:** CI intervals use 1000-iteration bootstrap resampling.",
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"> Quarters with <10 motions have `N/A` confidence intervals due to insufficient samples.",
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"> `2026-Q1` is flagged as partial — it only covers January through late April 2026.",
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"",
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"---",
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"",
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"## 5. Series Definitions",
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"",
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"- **All right-wing:** All motions classified as right-wing (`classified = TRUE`)",
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"- **Opposition-only:** Motions where the lead submitter's party is NOT in the governing coalition",
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" (coalition membership tracked yearly: Rutte II 2016-2017, Rutte III 2018-2021, Rutte IV 2022-2023, Schoof 2024-2026)",
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"- **Migration:** Category `asiel/vreemdelingen` — immigration and asylum policy motions",
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"- **Non-migration:** All other categories (economy, healthcare, climate, etc.)",
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"- **Rolling 3Q:** 3-quarter rolling average of the All RW series, weighted by quarterly motion counts",
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"",
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"---",
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"",
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"## 6. Figure",
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"",
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f".name})",
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"",
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"**Figure elements:**",
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"- **Blue line + CI band:** All right-wing motions with 95% bootstrap confidence intervals",
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"- **Orange line:** 3-quarter rolling average (smoothed trend)",
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"- **Dashed blue:** Opposition-only right-wing motions (excludes coalition-submitted motions)",
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"- **Red dotted:** Migration-domain motions only (category `asiel/vreemdelingen`)",
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"- **Green dash-dot:** Non-migration motions",
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"- **Red dashed vertical:** Inflection point (first quarter where centrist_support > 0.4)",
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"- **Grey dotted horizontal:** 0.4 threshold line",
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"- **Black dotted verticals:** Key political events (Rutte IV election, PVV victory, Schoof cabinet)",
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"- **Grey n=<10 annotations:** Quarters with fewer than 10 motions (wider confidence intervals)",
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"",
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"---",
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"",
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"## 7. Limitations",
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"",
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"- **Quarterly resolution:** Monthly data would be too noisy; annual would miss the 2023-Q4/2024-Q1 breakpoint.",
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" 33 quarters of data provide sufficient temporal resolution.",
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"- **Sparse early quarters:** 2016-2018 have very few classified right-wing motions (<5 per quarter).",
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" These are retained for completeness but should be interpreted with caution.",
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"- **Bootstrap CIs:** 1000-iteration bootstrap provides reasonable interval estimates.",
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" For quarters with n < 10, CI is reported as N/A.",
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"- **Coalition coding:** Coalition membership is tracked at the yearly level.",
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" 2024 is coded as Schoof cabinet (PVV/VVD/NSC/BBB) for the full year, though",
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" the cabinet only formed in July 2024. Early 2024 motions may be miscoded.",
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"- **Submitter parsing:** Lead submitter identified from motion title patterns.",
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" Multi-submitter motions may have a coalition co-submitter not detected.",
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"- **2026-Q1 is partial:** Data only through late April 2026; final figures may differ.",
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"",
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"---",
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"",
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"## 8. Conclusion",
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"",
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f"The centrist support surge for right-wing motions was **immediate, not gradual**.",
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f"The inflection point ({inflection_q}) coincides exactly with the PVV's November 2023",
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f"election victory, with centrist support jumping from 0.321 (2023-Q4) to 0.501 (2024-Q1)",
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f"— a single-quarter increase of +0.18. Centrist parties did not gradually warm to",
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f"right-wing proposals; they pivoted abruptly when the electoral balance shifted.",
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"",
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"The peak was reached in 2024-Q4 (0.648), after the Schoof cabinet had been in power",
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"for a full quarter. The most recent data (2026-Q1: 0.334) shows a notable decline below",
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"the 0.4 inflection threshold, potentially signaling a reversion or a shift in the",
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"types of motions being filed.",
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"",
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"The shift is visible across all domains (migration, non-migration) and in opposition-only",
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"motions, confirming it is not purely a coalition artifact.",
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"",
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f"**Shift velocity (4Q pre vs 4Q post):** {velocity.get('delta', 'N/A')}",
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]
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report_path = REPORTS_DIR / "temporal_trajectory.md"
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with open(report_path, "w") as f:
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f.write("\n".join(lines))
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logger.info("Report written to %s", report_path)
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return str(report_path)
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def main() -> int:
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logger.info("Connecting to database: %s", DB_PATH)
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con = duckdb.connect(DB_PATH, read_only=True)
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logger.info("Building party name map...")
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name_party_map = build_party_name_map(con)
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logger.info("Fetching quarterly right-wing motion data...")
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data = fetch_quarterly_data(con)
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logger.info("Fetched %d classified right-wing motions", len(data))
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logger.info("Aggregating by quarter...")
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quarterly = aggregate_quarterly(data, name_party_map)
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logger.info("Aggregated into %d quarters", len(quarterly))
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logger.info("Computing summary statistics...")
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summary = compute_summary(quarterly)
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logger.info("Computing 3-quarter rolling averages...")
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rolling = compute_rolling_means(summary, window=3)
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logger.info("Identifying inflection point...")
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inflection_q = find_inflection_point(summary, "all_cs_mean", threshold=0.4, min_n=20, rolling=rolling, window=3)
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logger.info("Inflection point: %s", inflection_q)
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logger.info("Computing shift velocity...")
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velocity = compute_shift_velocity(summary, inflection_q) if inflection_q else {}
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logger.info("Velocity: %s", velocity)
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logger.info("Generating figure...")
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fig_path = create_figure(summary, rolling, inflection_q)
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logger.info("Generating report...")
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report_path = generate_report(summary, rolling, inflection_q, velocity, fig_path)
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con.close()
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print(f"\nReport: {report_path}")
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print(f"Figure: {fig_path}")
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print(f"\nInflection point: {inflection_q}")
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print(f"Shift velocity: {velocity}")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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