Authority axis v1: mint the executive branch + link 27,804 § → office edges
The Code empowers the executive branch, but the mirror only had legislative Bodies — every authority reference pointed at nothing. Fixed by minting the executive branch from the Code's OWN enumerations: 15 Cabinet departments (5 U.S.C. § 101) + 21 Executive-Schedule Level I offices (§ 5312), 42 Body nodes under us/executive/. Then the axis: scripts/extract_authority.py matches named office/department references across all 59,740 sections (guarding against Deputy/Assistant/Under subordinates) and emits data/section_authority_edges.jsonl — 27,804 edges from 17,031 sections (28.7% of the Code) to the offices they empower. build.py renders reciprocal 'empowered by' links on each office node; the section files are never touched. Now: click the Attorney General, see all 2,386 sections that vest authority in it. Named references only in v1 (cabinet-level). Deterministic; body.schema classification enum extended (executive-department/-agency/-office); make check green at 105,746 records. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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#!/usr/bin/env python3
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"""Extract the authority axis: U.S. Code section -> executive office/department.
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Named-reference extraction (v1): matches the canonical office and department
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names in data/executive_offices.jsonl against each section's operative text
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(the '## Text' body, excluding historical Notes), and emits one edge per
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(section, target) into data/section_authority_edges.jsonl.
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Deterministic and mechanical — no NLP, no network. This is a committed input,
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generated like county_district_edges.jsonl; scripts/build.py renders it into
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reciprocal links on the executive office nodes (the section files are never
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touched). Relative references ("the Secretary") are out of scope for v1.
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Usage: python3 scripts/extract_authority.py
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"""
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import json
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import re
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from pathlib import Path
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REPO = Path(__file__).resolve().parent.parent
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OFFICES = REPO / "data" / "executive_offices.jsonl"
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CODE = REPO / "legal" / "us" / "code"
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OUT = REPO / "data" / "section_authority_edges.jsonl"
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# An alias immediately preceded by one of these is a *different*, lower office
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# (a Deputy/Assistant/Under Secretary, a Solicitor/Inspector General, etc.) —
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# not the cabinet-rank principal, so it must not match.
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SUBORDINATE_PREFIX = re.compile(
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r"(?:Deputy|Assistant|Associate|Under|Acting|Solicitor|Inspector|Principal|"
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r"Special|Vice|Former|Additional)\s*$")
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def load_targets():
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"""Return alias-matchers sorted longest-first: (regex, target_id, name, kind, rel)."""
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targets = []
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for line in OFFICES.open():
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r = json.loads(line)
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dept_id = f"us/executive/{r['slug']}"
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office_id = f"{dept_id}/{r['head']['slug']}"
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rel_kind = "references" # a department/agency mention
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for alias in r["aliases"]:
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targets.append((alias, dept_id, r["name"], r["kind"], rel_kind))
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for alias in r["head"]["aliases"]: # the officer the law empowers
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targets.append((alias, office_id, r["head"]["name"], "office", "empowers"))
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# longest alias first so "Secretary of the Treasury" wins over any substring
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targets.sort(key=lambda t: -len(t[0]))
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return [(re.compile(r"\b" + re.escape(a) + r"\b"), tid, name, kind, rel)
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for a, tid, name, kind, rel in targets]
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def operative_text(md):
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"""The section's live text only — frontmatter and historical Notes dropped."""
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parts = md.split("---", 2)
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body = parts[2] if len(parts) >= 3 else md
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return re.split(r"\n##\s+Notes", body, maxsplit=1)[0]
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def frontmatter_field(md, key):
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m = re.search(rf"(?m)^{key}:\s*(.+)$", md.split("---", 2)[1] if "---" in md else md)
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if not m:
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return None
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v = m.group(1).strip()
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return v[1:-1] if len(v) >= 2 and v[0] == v[-1] == '"' else v
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def main():
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targets = load_targets()
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edges = []
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for path in sorted(CODE.rglob("section-*.md")):
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md = path.read_text(encoding="utf-8")
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text = operative_text(md)
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section = frontmatter_field(md, "source_identifier")
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citation = frontmatter_field(md, "citation")
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title_num = frontmatter_field(md, "title_number")
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if not section:
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continue
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rel_path = str(path.relative_to(REPO))
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# (target_id) -> [name, kind, rel, mentions]; keep the most-specific hit
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hits = {}
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for rx, tid, name, kind, rel in targets:
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occ = 0
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for m in rx.finditer(text):
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pre = text[max(0, m.start() - 24):m.start()]
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if SUBORDINATE_PREFIX.search(pre):
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continue
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occ += 1
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if occ:
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cur = hits.get(tid)
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if cur is None:
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hits[tid] = [name, kind, rel, occ]
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else:
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cur[3] += occ
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for tid, (name, kind, rel, mentions) in hits.items():
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edges.append({
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"section": section,
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"citation": citation,
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"path": rel_path,
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"title": int(title_num) if title_num and title_num.isdigit() else None,
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"target": tid,
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"target_name": name,
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"target_kind": kind,
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"relationship": rel,
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"mentions": mentions,
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})
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edges.sort(key=lambda e: (e["section"], e["target"]))
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with OUT.open("w", encoding="utf-8") as f:
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for e in edges:
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f.write(json.dumps(e, ensure_ascii=False) + "\n")
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sections = len({e["section"] for e in edges})
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offices = len({e["target"] for e in edges if e["target_kind"] == "office"})
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print(f"authority edges: {len(edges)} "
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f"(sections with authority: {sections}; distinct targets hit: {len({e['target'] for e in edges})}; "
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f"office targets: {offices})")
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print(f"wrote {OUT.relative_to(REPO)}")
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if __name__ == "__main__":
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main()
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