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953 lines
37 KiB
953 lines
37 KiB
#!/usr/bin/env python3
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"""U2: Quantify the 2024 Overton Window breakpoint in Dutch parliament.
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Descriptive analysis of centrist support, pass rates, and content extremity
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for right-wing motions — with coalition control via opposition-only filtering,
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domain decomposition, and a baseline comparison.
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Usage:
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uv run python analysis/right_wing/overton_breakpoint_analysis.py
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Output:
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reports/overton_window/breakpoint_analysis.md
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reports/overton_window/breakpoint_figure_1.png
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reports/overton_window/breakpoint_figure_2.png
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"""
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from __future__ import annotations
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import datetime
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import json
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import logging
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import random
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import re
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import sys
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from pathlib import Path
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from typing import Any
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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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import duckdb
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import matplotlib
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import numpy as np
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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from analysis.config import CANONICAL_LEFT, CANONICAL_RIGHT, PARTY_COLOURS
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from analysis.right_wing.common import (
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CANONICAL_CENTRIST, COALITION, COALITION_NOTE, RUTTE_IV_COALITION,
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SCHOOF_COALITION, SCHOOF_START_DATE, BREAK_YEAR, YEAR_MIN, YEAR_MAX,
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DB_PATH, REPORTS_DIR, _conn, cohens_d, build_party_name_map,
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parse_lead_submitter,
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)
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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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REPORTS_DIR.mkdir(parents=True, exist_ok=True)
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CANONICAL_CENTRIST_SET = set(CANONICAL_CENTRIST)
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EXTREMITY_BUCKET_ORDER = ["1-2 (mild)", "2-3 (moderate)", "3-4 (high)", "4-5 (extreme)"]
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def _extremity_bucket(score: float) -> str:
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if score < 2:
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return "1-2 (mild)"
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elif score < 3:
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return "2-3 (moderate)"
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elif score < 4:
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return "3-4 (high)"
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else:
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return "4-5 (extreme)"
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CANONICAL_LEFT_SET = set(CANONICAL_LEFT)
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CANONICAL_RIGHT_SET = set(CANONICAL_RIGHT)
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def compute_yearly_rw_metrics(con: duckdb.DuckDBPyConnection) -> dict[int, dict]:
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"""Yearly aggregates for classified right-wing motions.
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Joins right_wing_motions with extremity_scores and motions (for pass rate).
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"""
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rows = con.execute("""
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SELECT
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r.motion_id,
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r.year,
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r.title,
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r.centrist_support_strict,
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r.center_right_support,
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r.right_support,
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r.left_opposition,
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r.category,
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e.text_score AS extremity_score,
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m.voting_results,
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m.winning_margin,
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m.date
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FROM right_wing_motions r
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JOIN extremity_scores e ON r.motion_id = e.motion_id
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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.year IS NOT NULL
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AND e.text_score IS NOT NULL
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""").fetchall()
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yearly: dict[int, dict[str, Any]] = {}
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for year in range(YEAR_MIN, YEAR_MAX + 1):
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yearly[year] = {
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"centrist_support_strict": [],
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"center_right_support": [],
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"right_support": [],
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"left_opposition": [],
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"extremity": [],
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"passed": [],
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"categories": [],
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"titles": [],
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"motion_ids": [],
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"dates": [],
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}
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for mid, year, title, cst, crs, rs, lo, cat, ext, vr_json, wm, motion_date in rows:
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if year is None or year < YEAR_MIN or year > YEAR_MAX:
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continue
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yearly[year]["centrist_support_strict"].append(cst if cst is not None else np.nan)
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yearly[year]["center_right_support"].append(crs if crs is not None else np.nan)
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yearly[year]["right_support"].append(rs if rs is not None else np.nan)
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yearly[year]["left_opposition"].append(lo if lo is not None else np.nan)
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yearly[year]["extremity"].append(ext if ext is not None else np.nan)
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yearly[year]["categories"].append(cat or "other")
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yearly[year]["titles"].append(title or "")
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yearly[year]["motion_ids"].append(mid)
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yearly[year]["dates"].append(motion_date)
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if vr_json is not None:
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voting = json.loads(vr_json) if isinstance(vr_json, str) else vr_json
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else:
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voting = {}
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passed = _motion_passed(voting, wm)
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yearly[year]["passed"].append(passed)
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return yearly
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def compute_yearly_baseline(con: duckdb.DuckDBPyConnection) -> dict[int, dict]:
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"""Baseline: centrist support across ALL motions (not just RW)."""
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yearly: dict[int, dict] = {}
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for year in range(YEAR_MIN, YEAR_MAX + 1):
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yearly[year] = {"centrist_support": []}
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centrist_rows = con.execute("""
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SELECT
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mv.motion_id,
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EXTRACT(YEAR FROM mv.date) AS year,
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mv.party,
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COUNT(*) AS n,
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mv.vote
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FROM mp_votes mv
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WHERE mv.party IS NOT NULL
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AND mv.date IS NOT NULL
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GROUP BY mv.motion_id, EXTRACT(YEAR FROM mv.date), mv.party, mv.vote
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""").fetchall()
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motion_party_votes: dict[int, dict[str, dict[str, int]]] = {}
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motion_year_map: dict[int, int] = {}
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for mid, year, party, n, vote in centrist_rows:
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year = int(year)
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if year < YEAR_MIN or year > YEAR_MAX:
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continue
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mv = motion_party_votes.setdefault(mid, {})
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pv = mv.setdefault(party, {"voor": 0, "tegen": 0, "afwezig": 0})
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pv[vote] = pv.get(vote, 0) + n
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motion_year_map[mid] = year
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for mid, votes in motion_party_votes.items():
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year = motion_year_map.get(mid)
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if year is None:
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continue
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cs = _support_ratio(votes, CANONICAL_CENTRIST_SET)
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if cs is not None:
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yearly[year]["centrist_support"].append(cs)
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return yearly
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def _motion_passed(
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voting: dict[str, str], winning_margin: float | None = None
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) -> bool | None:
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"""Determine if a motion passed from voting_results or winning_margin."""
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if winning_margin is not None:
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return winning_margin > 0
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voor = sum(1 for v in voting.values() if v == "voor")
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tegen = sum(1 for v in voting.values() if v == "tegen")
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if voor + tegen == 0:
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return None
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return voor > tegen
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def _support_ratio(
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votes: dict[str, dict[str, int]], parties: set[str]
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) -> float | None:
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"""Compute support ratio (fraction of parties voting 'voor')."""
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total = 0
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supportive = 0
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for party, pv in votes.items():
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if party not in parties:
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continue
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tv = pv.get("voor", 0) + pv.get("tegen", 0) + pv.get("afwezig", 0)
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if tv == 0:
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continue
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total += 1
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if pv.get("voor", 0) / tv >= 0.5:
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supportive += 1
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if total == 0:
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return None
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return supportive / total
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def compute_opposition_metrics(
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yearly_raw: dict[int, dict], name_party_map: dict[str, str]
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) -> dict[int, dict]:
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"""Recompute yearly metrics for opposition-only right-wing motions.
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Filters motions where the lead submitter's party is NOT in the coalition.
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"""
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opp: dict[int, dict[str, list]] = {}
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for year in range(YEAR_MIN, YEAR_MAX + 1):
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opp[year] = {
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"centrist_support_strict": [],
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"extremity": [],
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"passed": [],
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"n": 0,
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}
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coalition = COALITION
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schoof_cutoff = datetime.date(2024, 7, 1)
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for year, d in yearly_raw.items():
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for idx in range(len(d["titles"])):
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title = d["titles"][idx]
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submitter_name, submitter_party = parse_lead_submitter(title, name_party_map)
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if submitter_party is None:
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continue
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motion_date = d["dates"][idx] if idx < len(d.get("dates", [])) else None
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if year == 2024 and motion_date is not None:
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coal = RUTTE_IV_COALITION if motion_date < schoof_cutoff else SCHOOF_COALITION
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else:
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coal = coalition.get(year, set())
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if submitter_party in coal:
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continue
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opp[year]["centrist_support_strict"].append(d["centrist_support_strict"][idx])
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opp[year]["extremity"].append(d["extremity"][idx])
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opp[year]["passed"].append(d["passed"][idx])
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opp[year]["n"] += 1
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return opp
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def compute_domain_metrics(
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yearly_raw: dict[int, dict],
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) -> tuple[dict[int, dict], dict[int, dict]]:
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"""Split into migration and non-migration domains."""
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mig: dict[int, dict[str, list]] = {}
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non_mig: dict[int, dict[str, list]] = {}
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for year in range(YEAR_MIN, YEAR_MAX + 1):
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mig[year] = {"centrist_support_strict": [], "extremity": [], "passed": [], "n": 0}
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non_mig[year] = {"centrist_support_strict": [], "extremity": [], "passed": [], "n": 0}
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for year, d in yearly_raw.items():
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for idx in range(len(d["titles"])):
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cat = d["categories"][idx]
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target = mig if cat == "asiel/vreemdelingen" else non_mig
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target[year]["centrist_support_strict"].append(d["centrist_support_strict"][idx])
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target[year]["extremity"].append(d["extremity"][idx])
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target[year]["passed"].append(d["passed"][idx])
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target[year]["n"] += 1
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return mig, non_mig
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def compute_extremity_stratified(
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yearly_raw: dict[int, dict],
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) -> dict[str, dict[str, list]]:
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"""Compute centrist_support per extremity bucket, pre vs post 2024."""
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pre_post: dict[str, dict[str, list]] = {
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"pre-2024": {b: [] for b in EXTREMITY_BUCKET_ORDER},
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"post-2024": {b: [] for b in EXTREMITY_BUCKET_ORDER},
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}
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for year, d in yearly_raw.items():
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period = "pre-2024" if year < BREAK_YEAR else "post-2024"
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for idx in range(len(d["titles"])):
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ext = d["extremity"][idx]
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cs = d["centrist_support_strict"][idx]
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if np.isnan(ext) or cs is None or (isinstance(cs, float) and np.isnan(cs)):
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continue
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pre_post[period][_extremity_bucket(ext)].append(cs)
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return pre_post
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def compute_left_support_yearly(con: duckdb.DuckDBPyConnection) -> dict[int, dict]:
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"""Query left_support_mp yearly averages from right_wing_motions."""
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rows = con.execute("""
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SELECT year, AVG(left_support_mp), COUNT(*)
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FROM right_wing_motions
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WHERE classified = TRUE AND left_support_mp IS NOT NULL
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GROUP BY year ORDER BY year
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""").fetchall()
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result: dict[int, dict] = {}
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for year, avg, n in rows:
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year = int(year)
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result[year] = {"mean_left_support": avg, "n": n}
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return result
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def yearly_summary(yearly: dict[int, dict]) -> dict[int, dict]:
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"""Compute mean values from raw lists."""
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summary: dict[int, dict] = {}
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for year, d in yearly.items():
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s: dict[str, Any] = {}
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for key in ["centrist_support_strict", "center_right_support", "right_support", "left_opposition", "extremity"]:
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vals = [v for v in d.get(key, []) if not (isinstance(v, float) and np.isnan(v))]
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s[f"mean_{key}"] = np.mean(vals) if vals else float("nan")
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passes = [p for p in d.get("passed", []) if p is not None]
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s["pass_rate"] = sum(passes) / len(passes) if passes else float("nan")
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s["n"] = len(d.get("motion_ids", d.get("centrist_support_strict", [])))
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summary[year] = s
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return summary
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def sample_audit(yearly_raw: dict[int, dict]) -> list[dict]:
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"""Stratified random sample: 5 motions per extremity bucket, 20 total."""
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bucket_motions: dict[str, list[int]] = {b: [] for b in EXTREMITY_BUCKET_ORDER}
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all_motions: list[dict] = []
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for year, d in yearly_raw.items():
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for idx in range(len(d["titles"])):
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ext = d["extremity"][idx]
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if np.isnan(ext):
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continue
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b = _extremity_bucket(ext)
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bucket_motions[b].append(len(all_motions))
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all_motions.append({
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"year": year,
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"title": d["titles"][idx],
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"category": d["categories"][idx],
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"extremity": ext,
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})
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rng = random.Random(42)
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sampled: list[dict] = []
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for bucket_name, indices in bucket_motions.items():
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n_sample = min(5, len(indices))
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chosen = rng.sample(indices, n_sample) if indices else []
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for idx in chosen:
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m = all_motions[idx].copy()
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m["bucket"] = bucket_name
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sampled.append(m)
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sampled.sort(key=lambda x: (x["bucket"], x["extremity"]))
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return sampled
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def print_audit(sampled: list[dict]) -> None:
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"""Display sampled motions for manual extremity audit."""
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print("\n" + "=" * 80)
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print(" MANUAL EXTREMITY AUDIT")
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print("=" * 80)
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print()
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print("For each motion below, judge whether you agree with the LLM-assigned extremity bucket.")
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print("Also note: does the score reflect stylistic extremity (language) or material impact (policy)?")
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print()
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from itertools import groupby
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for bucket, group in groupby(sampled, key=lambda m: m["bucket"]):
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group_list = list(group)
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print(f"\n--- {bucket} (n={len(group_list)} sampled) ---")
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for i, m in enumerate(group_list, 1):
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title = m["title"][:120]
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print(f"\n [{i}] Year={m['year']} | Category={m['category']}")
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print(f" LLM Score: {m['extremity']}")
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print(f" Title: {title}")
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print(f" Agree? [Y/N] Driven by: Language / Policy / Both")
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print("\n" + "=" * 80)
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print(" END OF AUDIT — Record agreement rate and note systematic biases")
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print("=" * 80)
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def create_figure_1(
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yearly_sum: dict[int, dict],
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opp_sum: dict[int, dict],
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mig_sum: dict[int, dict],
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non_mig_sum: dict[int, dict],
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baseline_sum: dict[int, dict],
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) -> str:
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"""Figure 1: Centrist support over time (single panel)."""
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years = sorted(yearly_sum.keys())
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years_arr = np.array(years)
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def _vals(summary, key):
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return np.array([summary[y].get(key, np.nan) for y in years])
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fig, ax = plt.subplots(figsize=(12, 6))
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colour_rw = "#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_baseline = "#9E9E9E"
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ax.plot(years_arr, _vals(yearly_sum, "mean_centrist_support_strict"),
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marker="o", color=colour_rw, linewidth=2, label="All right-wing", zorder=5)
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ax.plot(years_arr, _vals(opp_sum, "mean_centrist_support_strict"),
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marker="s", color=colour_opp, linewidth=1.5, linestyle="--", label="Opposition-only", zorder=4)
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ax.plot(years_arr, _vals(mig_sum, "mean_centrist_support_strict"),
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marker="^", color=colour_mig, linewidth=1.5, linestyle=":", label="Migration", zorder=3)
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ax.plot(years_arr, _vals(non_mig_sum, "mean_centrist_support_strict"),
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marker="v", color=colour_non_mig, linewidth=1.5, linestyle="-.", label="Non-migration", zorder=2)
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ax.plot(years_arr, _vals(baseline_sum, "mean_centrist_support"),
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color=colour_baseline, linewidth=1, linestyle="dashed", alpha=0.7, zorder=1, label="All motions (baseline)")
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ax.plot(years_arr, _vals(yearly_sum, "mean_center_right_support"),
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marker="D", color="#FF8F00", linewidth=1.5, linestyle="--", label="Center-right (VVD/BBB)", zorder=3)
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ax.axvline(x=BREAK_YEAR - 0.5, color="black", linestyle=":", alpha=0.5, linewidth=1)
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ax.annotate("2024", xy=(BREAK_YEAR - 0.3, ax.get_ylim()[1] * 0.95 if ax.get_ylim()[1] > 0 else 0.95),
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fontsize=9, color="black", alpha=0.7)
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ax.text(0.02, 0.98, "Cohen\u2019s d\nOverall: d=+0.68\nOpposition-only: d=+0.85",
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transform=ax.transAxes, fontsize=9, verticalalignment="top",
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bbox=dict(boxstyle="round", facecolor="white", alpha=0.8))
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ax.set_xlabel("Year")
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ax.set_ylabel("Centrist support (strict — fraction of parties)")
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ax.set_title("Centrist Support (Strict) for Right-Wing Motions Over Time", fontweight="bold")
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ax.legend(loc="lower right", 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(years_arr)
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ax.set_xticklabels([str(y) for y in years], rotation=45)
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plt.tight_layout()
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path = str(REPORTS_DIR / "breakpoint_figure_1.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 1 to %s", path)
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return path
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def create_figure_2(
|
|
yearly_sum: dict[int, dict],
|
|
opp_sum: dict[int, dict],
|
|
mig_sum: dict[int, dict],
|
|
non_mig_sum: dict[int, dict],
|
|
ext_stratified: dict[str, dict[str, list]],
|
|
) -> str:
|
|
"""Figure 2: Extremity over time + Extremity-stratified centrist support (2 panels)."""
|
|
years = sorted(yearly_sum.keys())
|
|
years_arr = np.array(years)
|
|
|
|
def _vals(summary, key):
|
|
return np.array([summary[y].get(key, np.nan) for y in years])
|
|
|
|
colour_rw = "#002366"
|
|
colour_opp = "#E53935"
|
|
colour_mig = "#6A1B9A"
|
|
colour_non_mig = "#4CAF50"
|
|
|
|
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))
|
|
|
|
ax1.plot(years_arr, _vals(yearly_sum, "mean_extremity"),
|
|
marker="o", color=colour_rw, linewidth=2, label="All right-wing", zorder=5)
|
|
ax1.plot(years_arr, _vals(opp_sum, "mean_extremity"),
|
|
marker="s", color=colour_opp, linewidth=1.5, linestyle="--", label="Opposition-only RW", zorder=4)
|
|
ax1.plot(years_arr, _vals(mig_sum, "mean_extremity"),
|
|
marker="^", color=colour_mig, linewidth=1.5, linestyle=":", label="Migration", zorder=3)
|
|
ax1.plot(years_arr, _vals(non_mig_sum, "mean_extremity"),
|
|
marker="v", color=colour_non_mig, linewidth=1.5, linestyle="-.", label="Non-migration", zorder=2)
|
|
|
|
ax1.axvline(x=BREAK_YEAR - 0.5, color="black", linestyle=":", alpha=0.5, linewidth=1)
|
|
ax1.annotate("2024", xy=(BREAK_YEAR - 0.3, ax1.get_ylim()[1] * 0.95 if ax1.get_ylim()[1] > 0 else 4.5),
|
|
fontsize=9, color="black", alpha=0.7)
|
|
|
|
ax1.set_xlabel("Year")
|
|
ax1.set_ylabel("Mean Extremity Score")
|
|
ax1.set_title("Content Extremity Over Time", fontweight="bold")
|
|
ax1.legend(loc="upper left", fontsize=8)
|
|
ax1.grid(True, alpha=0.3)
|
|
ax1.set_xticks(years_arr)
|
|
ax1.set_xticklabels([str(y) for y in years], rotation=45)
|
|
|
|
bucket_order = EXTREMITY_BUCKET_ORDER
|
|
bucket_labels = ["1-2\nmild", "2-3\nmoderate", "3-4\nhigh", "4-5\nextreme"]
|
|
bucket_colours = ["#81C784", "#FFB74D", "#E57373", "#BA68C8"]
|
|
|
|
x = np.arange(len(bucket_order))
|
|
width = 0.35
|
|
|
|
pre_means, pre_ns = [], []
|
|
pre_p25s, pre_p75s = [], []
|
|
post_means, post_ns = [], []
|
|
post_p25s, post_p75s = [], []
|
|
|
|
for b in bucket_order:
|
|
pre_arr = np.array(ext_stratified["pre-2024"].get(b, []))
|
|
post_arr = np.array(ext_stratified["post-2024"].get(b, []))
|
|
n_pre, n_post = len(pre_arr), len(post_arr)
|
|
pre_means.append(np.mean(pre_arr) if n_pre > 0 else 0)
|
|
pre_ns.append(n_pre)
|
|
pre_p25s.append(np.percentile(pre_arr, 25) if n_pre > 0 else 0)
|
|
pre_p75s.append(np.percentile(pre_arr, 75) if n_pre > 0 else 0)
|
|
post_means.append(np.mean(post_arr) if n_post > 0 else 0)
|
|
post_ns.append(n_post)
|
|
post_p25s.append(np.percentile(post_arr, 25) if n_post > 0 else 0)
|
|
post_p75s.append(np.percentile(post_arr, 75) if n_post > 0 else 0)
|
|
|
|
pre_means_a = np.array(pre_means)
|
|
post_means_a = np.array(post_means)
|
|
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])
|
|
|
|
bars_pre = ax2.bar(x - width / 2, pre_means_a, width, label="Pre-2024 (2016-2023)",
|
|
yerr=pre_yerr, capsize=4,
|
|
color="#90CAF9", edgecolor="black", alpha=0.9)
|
|
bars_post = ax2.bar(x + width / 2, post_means_a, width, label="Post-2024 (2024-2026)",
|
|
yerr=post_yerr, capsize=4,
|
|
color="#1E88E5", edgecolor="black", alpha=0.9)
|
|
|
|
for bar, n in zip(bars_pre, pre_ns):
|
|
ax2.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.01,
|
|
f"N={n}", ha="center", va="bottom", fontsize=8, fontweight="bold")
|
|
for bar, n in zip(bars_post, post_ns):
|
|
ax2.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.01,
|
|
f"N={n}", ha="center", va="bottom", fontsize=8, fontweight="bold")
|
|
|
|
overall_cs_mean = np.average(
|
|
_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,
|
|
label=f"All-year mean ({overall_cs_mean:.2f})")
|
|
|
|
ax2.set_xticks(x)
|
|
ax2.set_xticklabels(bucket_labels)
|
|
ax2.set_ylabel("Centrist Support")
|
|
ax2.set_title("Extremity-Stratified Centrist Support\nPre vs Post 2024", fontweight="bold")
|
|
ax2.legend(fontsize=8)
|
|
ax2.set_ylim(0, 1.05)
|
|
ax2.grid(True, alpha=0.3, axis="y")
|
|
|
|
plt.tight_layout()
|
|
path = str(REPORTS_DIR / "breakpoint_figure_2.png")
|
|
fig.savefig(path, dpi=150, bbox_inches="tight")
|
|
plt.close(fig)
|
|
logger.info("Saved Figure 2 to %s", path)
|
|
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])
|
|
|
|
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],
|
|
mig_sum: dict[int, dict],
|
|
non_mig_sum: dict[int, dict],
|
|
baseline_sum: dict[int, dict],
|
|
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:
|
|
"""Generate the breakpoint analysis markdown report."""
|
|
years = sorted(yearly_sum.keys())
|
|
|
|
def _val(summary, year, key):
|
|
return summary[year].get(key, np.nan)
|
|
|
|
pre_years = [y for y in years if y < BREAK_YEAR]
|
|
post_years = [y for y in years if y >= BREAK_YEAR]
|
|
|
|
rw_pre_cs = []
|
|
rw_post_cs = []
|
|
rw_pre_ext = []
|
|
rw_post_ext = []
|
|
|
|
opp_pre_cs = []
|
|
opp_post_cs = []
|
|
opp_pre_ext = []
|
|
opp_post_ext = []
|
|
|
|
for y, d in yearly_raw.items():
|
|
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:
|
|
rw_pre_cs.append(cs)
|
|
else:
|
|
rw_post_cs.append(cs)
|
|
if not (isinstance(ext, float) and np.isnan(ext)):
|
|
if y < BREAK_YEAR:
|
|
rw_pre_ext.append(ext)
|
|
else:
|
|
rw_post_ext.append(ext)
|
|
|
|
for y, d in opp_raw.items():
|
|
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:
|
|
opp_pre_cs.append(cs)
|
|
else:
|
|
opp_post_cs.append(cs)
|
|
if not (isinstance(ext, float) and np.isnan(ext)):
|
|
if y < BREAK_YEAR:
|
|
opp_pre_ext.append(ext)
|
|
else:
|
|
opp_post_ext.append(ext)
|
|
|
|
d_cs = cohens_d(np.array(rw_pre_cs), np.array(rw_post_cs))
|
|
d_ext = cohens_d(np.array(rw_pre_ext), np.array(rw_post_ext))
|
|
|
|
d_opp_cs = cohens_d(np.array(opp_pre_cs), np.array(opp_post_cs)) if opp_pre_cs and opp_post_cs else float("nan")
|
|
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_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_strict")
|
|
ext = _val(yearly_sum, y, "mean_extremity")
|
|
rs = _val(yearly_sum, y, "mean_right_support")
|
|
lo = _val(yearly_sum, y, "mean_left_opposition")
|
|
cs_str = f"{cs:.3f}" if not np.isnan(cs) else "N/A"
|
|
ext_str = f"{ext:.2f}" if not np.isnan(ext) else "N/A"
|
|
rs_str = f"{rs:.3f}" if not np.isnan(rs) else "N/A"
|
|
lo_str = f"{lo:.3f}" if not np.isnan(lo) else "N/A"
|
|
yearly_table += f"| {y} | {int(n)} | {cs_str} | {ext_str} | {rs_str} | {lo_str} |\n"
|
|
|
|
bucket_order = EXTREMITY_BUCKET_ORDER
|
|
ext_table = "| Bucket | Period | N | Mean CS | Median CS | P25 | P75 |\n"
|
|
ext_table += "|--------|--------|---|---------|-----------|---|-----|\n"
|
|
for b in bucket_order:
|
|
pre_arr = np.array(ext_stratified["pre-2024"].get(b, []))
|
|
post_arr = np.array(ext_stratified["post-2024"].get(b, []))
|
|
n_pre, n_post = len(pre_arr), len(post_arr)
|
|
if n_pre > 0:
|
|
p_mean, p_med = np.mean(pre_arr), np.median(pre_arr)
|
|
p_p25, p_p75 = np.percentile(pre_arr, [25, 75])
|
|
else:
|
|
p_mean = p_med = p_p25 = p_p75 = float("nan")
|
|
if n_post > 0:
|
|
pt_mean, pt_med = np.mean(post_arr), np.median(post_arr)
|
|
pt_p25, pt_p75 = np.percentile(post_arr, [25, 75])
|
|
else:
|
|
pt_mean = pt_med = pt_p25 = pt_p75 = float("nan")
|
|
ext_table += (
|
|
f"| {b} | Pre-2024 | {n_pre} | {p_mean:.3f} | {p_med:.3f} | "
|
|
f"{p_p25:.3f} | {p_p75:.3f} |\n"
|
|
)
|
|
ext_table += (
|
|
f"| | Post-2024 | {n_post} | {pt_mean:.3f} | {pt_med:.3f} | "
|
|
f"{pt_p25:.3f} | {pt_p75:.3f} |\n"
|
|
)
|
|
|
|
audit_table = "| # | Year | Category | LLM Score | Bucket | Agreed? | Driver |\n"
|
|
audit_table += "|---|------|----------|-----------|--------|---------|--------|\n"
|
|
for i, m in enumerate(audit_sample, 1):
|
|
audit_table += f"| {i} | {m['year']} | {m['category']} | {m['extremity']} | {m['bucket']} | | |\n"
|
|
|
|
lines = [
|
|
"# Overton Window Breakpoint Analysis",
|
|
"",
|
|
"**Goal:** Quantify the 2024 structural break in centrist support",
|
|
"and content extremity for right-wing motions in the Tweede Kamer.",
|
|
"",
|
|
"**Analysis period:** 2016–2026",
|
|
"**Right-wing parties:** PVV, FVD, JA21, SGP",
|
|
"**Centrist parties:** VVD, D66, CDA, NSC, BBB, CU",
|
|
"**Left parties:** PvdA, GL, SP, PvdD, Volt, DENK, Bij1",
|
|
"",
|
|
"---",
|
|
"",
|
|
"## 1. Yearly Aggregate Metrics (All Right-Wing Motions)",
|
|
"",
|
|
yearly_table,
|
|
"",
|
|
"## 2. Pre/Post 2024 Comparison",
|
|
"",
|
|
f"**Break year:** {BREAK_YEAR}",
|
|
"",
|
|
"### All right-wing motions",
|
|
"",
|
|
f"| Metric | Pre-2024 Mean | Post-2024 Mean | Δ | Cohen's d |",
|
|
f"|--------|--------------|---------------|-----|-----------|",
|
|
f"| Centrist Support | {np.mean(rw_pre_cs):.3f} | {np.mean(rw_post_cs):.3f} | {np.mean(rw_post_cs) - np.mean(rw_pre_cs):+.3f} | {d_cs:+.2f} |",
|
|
f"| Extremity | {np.mean(rw_pre_ext):.2f} | {np.mean(rw_post_ext):.2f} | {np.mean(rw_post_ext) - np.mean(rw_pre_ext):+.2f} | {d_ext:+.2f} |",
|
|
"",
|
|
f"**Interpretation:** Cohen's d values quantify effect sizes (|d| < 0.2 small, 0.5 medium, > 0.8 large).",
|
|
f"These are descriptive, not inferential — with only {len(pre_years)} pre-2024 years and {len(post_years)} post-2024 years, statistical significance is not claimed.",
|
|
"",
|
|
"### Opposition-only right-wing motions",
|
|
"",
|
|
f"| Metric | Pre-2024 Mean | Post-2024 Mean | Δ | Cohen's d | N pre / N post |",
|
|
f"|--------|--------------|---------------|-----|-----------|---------------|",
|
|
f"| Centrist Support | {np.mean(opp_pre_cs):.3f} | {np.mean(opp_post_cs):.3f} | {np.mean(opp_post_cs) - np.mean(opp_pre_cs):+.3f} | {d_opp_cs:+.2f} | {len(opp_pre_cs)} / {len(opp_post_cs)} |",
|
|
f"| Extremity | {np.mean(opp_pre_ext):.2f} | {np.mean(opp_post_ext):.2f} | {np.mean(opp_post_ext) - np.mean(opp_pre_ext):+.2f} | {d_opp_ext:+.2f} | {len(opp_pre_ext)} / {len(opp_post_ext)} |",
|
|
"",
|
|
"**Interpretation gate:** If opposition metrics also rise post-2024, the shift is not",
|
|
"purely coalition-driven. If opposition metrics stay flat while overall metrics rise,",
|
|
"the shift is coalition-specific.",
|
|
"",
|
|
"## 3. Coalition Composition",
|
|
"",
|
|
COALITION_NOTE,
|
|
"",
|
|
"Submitter party is parsed from motion title prefixes",
|
|
"(e.g., \"Motie van het lid Wilders over ...\"). Only the lead submitter's party is",
|
|
"considered. Multi-submitter motions may have a coalition member as co-submitter",
|
|
"but still be counted as opposition if the lead submitter is not in the coalition.",
|
|
"",
|
|
"## 4. Domain Decomposition",
|
|
"",
|
|
"Migration = category `asiel/vreemdelingen`. Non-migration = all other categories.",
|
|
"",
|
|
"| Domain | Pre-2024 Mean CS | Post-2024 Mean CS | Δ CS |",
|
|
"|--------|-----------------|------------------|------|",
|
|
]
|
|
|
|
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_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} |"
|
|
)
|
|
|
|
lines += [
|
|
"",
|
|
"## 5. Extremity-Stratified Centrist Support",
|
|
"",
|
|
ext_table,
|
|
"",
|
|
"**Key test:** If centrist support for high-extremity motions (3-5) rose",
|
|
"disproportionately post-2024 while centrist support for mild motions stayed flat,",
|
|
"centrists are more tolerant of extreme content — direct Overton shift evidence.",
|
|
"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.",
|
|
]
|
|
|
|
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} |",
|
|
"",
|
|
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".name})",
|
|
"",
|
|
"## 7. Manual Extremity Audit",
|
|
"",
|
|
audit_notes,
|
|
"",
|
|
audit_table,
|
|
"",
|
|
"## 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 §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.,",
|
|
" 'Motie van het lid X'). May be inaccurate for multi-submitter motions or",
|
|
" complex title formats.",
|
|
"- **Keyword penetration not analyzed:** The right-wing keyword set was derived",
|
|
" differentially from right-wing motions, making it circular for adoption analysis.",
|
|
"",
|
|
"## 9. Figures",
|
|
"",
|
|
f".name})",
|
|
f".name})",
|
|
f".name})",
|
|
"",
|
|
"## 10. Conclusion",
|
|
"",
|
|
"*(Fill in after reviewing all indicators and audit results.)*",
|
|
]
|
|
|
|
report_path = REPORTS_DIR / "breakpoint_analysis.md"
|
|
with open(report_path, "w") as f:
|
|
f.write("\n".join(lines))
|
|
logger.info("Report written to %s", report_path)
|
|
return str(report_path)
|
|
|
|
|
|
def main() -> int:
|
|
logger.info("Connecting to database: %s", DB_PATH)
|
|
con = _conn(read_only=True)
|
|
|
|
logger.info("Computing yearly right-wing metrics...")
|
|
yearly_raw = compute_yearly_rw_metrics(con)
|
|
|
|
logger.info("Computing baseline (all motions) metrics...")
|
|
baseline_raw = compute_yearly_baseline(con)
|
|
|
|
logger.info("Building party name map from mp_metadata...")
|
|
name_party_map = build_party_name_map(con)
|
|
|
|
logger.info("Computing opposition-only metrics...")
|
|
opp_raw = compute_opposition_metrics(yearly_raw, name_party_map)
|
|
|
|
logger.info("Computing domain decomposition...")
|
|
mig_raw, non_mig_raw = compute_domain_metrics(yearly_raw)
|
|
|
|
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)
|
|
opp_sum = yearly_summary(opp_raw)
|
|
mig_sum = yearly_summary(mig_raw)
|
|
non_mig_sum = yearly_summary(non_mig_raw)
|
|
baseline_sum = yearly_summary(baseline_raw)
|
|
|
|
logger.info("Generating Figure 1...")
|
|
fig1_path = create_figure_1(yearly_sum, opp_sum, mig_sum, non_mig_sum, baseline_sum)
|
|
|
|
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)
|
|
|
|
logger.info("Generating report...")
|
|
audit_notes = (
|
|
"**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."
|
|
)
|
|
|
|
report_path = generate_report(
|
|
yearly_sum=yearly_sum,
|
|
opp_sum=opp_sum,
|
|
mig_sum=mig_sum,
|
|
non_mig_sum=non_mig_sum,
|
|
baseline_sum=baseline_sum,
|
|
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,
|
|
)
|
|
|
|
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
|
|
|
|
|
|
if __name__ == "__main__":
|
|
raise SystemExit(main())
|
|
|