Audit Crossrefs

Phase 7: Cross-References

Convert hardcoded supra/infra note numbers to NOTEREF field codes that auto-update when footnotes are renumbered, then tie each cross-reference to a bibkey from references/sources.bib so the bibliography becomes the semantic identity layer.

Canonical 3-script pipeline

sources.bib ← make_bib_from_docx.py       # BOOTSTRAP (once per paper, Gemini)
                       ↓
docx ← create_crossrefs.py                # ONE-TIME conversion of hand-typed text
                       ↓
docx ← audit_crossref_targets.py --grep --apply        # DRIFT CORRECTION (deterministic)
                       ↓
docx ← bib_integrate.py --bib references/sources.bib   # BIBKEY-TAG SYNC (deterministic)
       (also pre-computes cached display values)

All four live in ${CLAUDE_SKILL_DIR}/../../../../skills/bluebook-audit/scripts/.

Maintenance one-liner

After a Word session that added/edited footnotes (including new hand-typed <X>, supra note N text), run:

"${CLAUDE_SKILL_DIR}/../../../../skills/bluebook-audit/scripts/sync_crossrefs.sh" \
  draft.docx references/sources.bib

The shell wrapper runs create_crossrefs → audit_crossref_targets → bib_integrate in sequence. All three are idempotent and fully deterministic — re-running on a clean doc does nothing; ~5 seconds total runtime.

Initial conversion (one-time)

Convert hardcoded <X>, supra note N text to NOTEREF cross-reference fields.

# Preview
uv run python3 "${CLAUDE_SKILL_DIR}/../../../../skills/bluebook-audit/scripts/create_crossrefs.py" --docx <path> --dry-run

# Apply
uv run python3 "${CLAUDE_SKILL_DIR}/../../../../skills/bluebook-audit/scripts/create_crossrefs.py" --docx <path>

Workflow

1. Bootstrap the bib (only if no references/sources.bib yet):

   uv run --with lxml --with google-genai --with google-cloud-storage python3 \
     "${CLAUDE_SKILL_DIR}/../../../../skills/bluebook-audit/scripts/make_bib_from_docx.py" \
     --docx <path> --out references/sources.bib
   

   Walks the docx footnotes, skips supra-only / bio fns, sends each first-cite candidate to Gemini Vertex Batch, emits BibTeX with note = {fnN} linking each entry to its source footnote.
2. Dry run create_crossrefs — review the cross-reference map and bookmark plan
3. Apply create_crossrefs — the script backs up the original before writing
4. Audit + retarget — run audit_crossref_targets.py --grep --apply (see below). Hand-typed supra note N numbers go stale as footnotes are added or reordered; the create_crossrefs script faithfully bookmarks the literal N, which can be silently wrong for every reference. Grep resolves the easy cases deterministically; only ambiguous refs need --batch.
5. Bibkey-tag — run bib_integrate.py --bib references/sources.bib --apply. Renames bookmarks to _RefBib_<bibkey>, retargets supras by bibkey, and pre-computes cached display values so the doc renders correctly on first read (no F9 needed).
6. Verify in Word — open the DOCX, spot-check 5-10 supras. Cmd+A → fn+F9 (Mac) or Ctrl+A → F9 (Windows) to force-refresh fields if needed.
7. Renumber test (optional) — add a footnote before a referenced target and confirm the supra numbers update.

audit_crossref_targets.py (drift correction, also runnable standalone)

# Mechanical audit only — flags refs whose surnames don't appear in the target footnote
uv run --with lxml python3 \
  "${CLAUDE_SKILL_DIR}/../../../../skills/bluebook-audit/scripts/audit_crossref_targets.py" \
  --docx <path>

# Deterministic first pass — grep each reference for its first cite. No LLM cost.
#   On a 138-ref test set: resolved 62 refs unique with 98% accuracy, deferred
#   76 truly ambiguous cases. Always run this BEFORE invoking Gemini/Batch — it
#   strips the easy cases and saves LLM calls.
uv run --with lxml python3 \
  "${CLAUDE_SKILL_DIR}/../../../../skills/bluebook-audit/scripts/audit_crossref_targets.py" \
  --docx <path> --grep --apply

# Grep + production LLM batch for the residue (recommended for full pipeline)
uv run --with lxml --with google-genai --with google-cloud-storage python3 \
  "${CLAUDE_SKILL_DIR}/../../../../skills/bluebook-audit/scripts/audit_crossref_targets.py" \
  --docx <path> --grep --batch --apply --location global

# LLM-only paths (skip grep — coverage-risky for --gemini):
uv run … --docx <path> --batch --model gemini-3.1-flash-lite-preview --apply
uv run … --docx <path> --gemini --apply

bib_integrate.py (bibkey-tag sync, fully deterministic)

uv run --with lxml python3 \
  "${CLAUDE_SKILL_DIR}/../../../../skills/bluebook-audit/scripts/bib_integrate.py" \
  --docx <path> --bib references/sources.bib --apply

What it does:

1. Parses sources.bib.
2. For each bib entry, derives signature tokens (author surnames + distinctive title words + institutional bibkey prefix) and finds the docx footnote whose body text contains all of them. This re-derives the fn_id → bibkey map from current docx content — sidesteps any drift between bib's note={fnN} tag and current numbering.
3. For each supra/infra NOTEREF, picks the best matching bibkey (target-fn bonus + global token scoring).
4. Creates _RefBib_<bibkey> bookmarks wrapping body footnoteReferences. Multi-cite footnotes get multiple bookmarks (one per source) — each pointing at the same body footnoteReference, so the displayed number stays the same; only the bookmark name differs by source.
5. Rewrites each NOTEREF instr text to point at its bibkey-named bookmark.
6. Pre-computes cached NOTEREF display values (using --bio-count, default 3 for *, †, ‡ author bios that don't consume display numbers when numRestart=eachSect is set).
7. Emits SUPRA_BIB_AUDIT.md listing any supras whose surnames don't match the picked bibkey's haystack (author + title + howpublished + bibkey).

Fully deterministic — no LLM calls. Idempotent on re-runs.

make_bib_from_docx.py (bootstrap, Gemini)

uv run --with lxml --with google-genai --with google-cloud-storage python3 \
  "${CLAUDE_SKILL_DIR}/../../../../skills/bluebook-audit/scripts/make_bib_from_docx.py" \
  --docx <path> --out references/sources.bib

Walks the docx footnotes, splits multi-cite footnotes on ;, sends each first-cite candidate to a Vertex AI Batch job (one independent request per citation; gemini-3.1-flash-lite-preview default). Emits BibTeX entries with bibkey conventions firstauthorlastYEAR for academic works and short slugs (gao2017, crs2024, secReg2020) for institutional sources.

Each entry includes note = {fnN} linking back to the source footnote.

Once sources.bib exists, maintain it directly — do not regenerate. Edits to the bib (typos, missing fields, new sources) should be in-place.

Vertex AI batch requires:

• gcloud auth application-default login for ADC
• --project (default $GOOGLE_CLOUD_PROJECT or activist-defense-nal)
• --gcs-bucket (default $GEMINI_BATCH_BUCKET or nal-batch-extraction)
• Location: us-central1 (default)

For gemini-3.x models, thinkingLevel: MINIMAL is set automatically — without it, batch responses silently return empty content (see /gemini-batch SKILL gotcha 12).

Output written to <docx-dir>/scratch/:

• crossref_audit.json — every cross-reference with current target + match status
• crossref_remap.json — Gemini's proposed corrections (with confidence)
• CROSSREF_AUDIT.md — human-readable diff report

The audit is safe to run standalone without re-running the rest of the bluebook-audit workflow. Use it any time create_crossrefs has been run and you want to validate that hand-typed supra/infra numbers were correct.

Recommended pipeline order

1. --grep first. Cheapest, deterministic, ~98% precision on unique matches. Resolves ~half the refs at $0 cost.
2. --batch (Vertex AI, per-ref independence) on the deferred set.
3. Manual first-cite verification on remaining mechanical flags (Step 4 below).

How --grep works

A cross-reference reads <short-form identifier>, supra/infra note N. The identifier can be any Bluebook short form, not just author surnames:

• author group: Hu, Malenko & Zytnick
• hereinafter tag: GAO Report, CRS Report, Rosenberg, 2020 SEC Regulation
• institutional doc: Best Practice Principles, Policies and Procedures, Senate Banking Committee Letter
• case name: ISS v. SEC
• title fragment: Choi, Fisch & Kahan, Power of Proxy Advisors

For each reference the script:

1. Tokenizes the identifier (filters Bluebook signals like See, But see, E.g.).
2. Strips every <X>, supra/infra note <N> phrase from each candidate footnote, so a footnote that merely cites the same identifier via supra doesn't count as the first cite.
3. Searches every footnote — except the source — for one whose post-supra-strip text contains all the identifier tokens with word-boundary matches.
4. If exactly one footnote matches → unique, apply deterministically.
5. If multiple match → ambiguous, defer to LLM (picking the earliest is wrong ~17% of the time — typically when the identifier appears in passing in one footnote and as a first cite in another).
6. If none match → no_match, defer to LLM (which has the catalog and can guess, or correctly say "not in document").

Step 4 (mandatory): Manual first-cite verification on residuals

Iron Law: Neither Gemini pass is fully trustworthy on its own. A two-batch consensus is high-confidence, but the residual disagreements ALWAYS require human first-cite verification. Do not stop after --apply.

The failure modes we observed on a real 248-footnote draft:

• Single-batched call: model returns uniform "medium" confidence — a smoking-gun signal it didn't reason per-reference.
• Per-reference Vertex Batch: model returns uniform "high" with detailed reasoning, but still confidently picks wrong targets when:
  • the cited work isn't in the doc (model hallucinates a plausible-sounding nearby match)
  • the first cite is buried mid-footnote in a See also or multi-cite list (model misses it and picks a different paper by the same author)
  • the reference is to a self-referencing pattern (model returns the source footnote itself)
• Mechanical surname check: only inspects the leading 250 chars of the target footnote, so first cites buried after See/hereinafter/inline-discussion lead-ins are flagged as mismatches even when correct.

Recipe

1. Compare both Gemini passes, taking the intersection where they agree as the high-confidence consensus. Apply that subset first.
2. For each disagreement, search the doc for the first non-supra cite of the disputed surname / title fragment. The script's regex below catches mid-footnote occurrences:

   for fn in fns.findall('.//w:footnote'):
       txt = ''.join(t.text or '' for t in fn.iter('w:t'))
       if re.search(r'<surname-pattern>', txt) and 'supra' not in txt[:300].lower():
           print(fn.get('w:id'), txt[:200])
   

3. Build a manual remap of (source_fn, surnames, current_bookmark) → correct_fn_id, save as crossref_remap_manual.json, and apply with --remap PATH --apply.
4. For the "no first cite in document" residue (works the author cites without ever having introduced):
   • Search broadly first — the first cite is often mid-footnote in a See also/multi-cite list.
   • Only if truly absent: flag for an author edit. Write SUPRA_UNRESOLVED.md listing the source FN, surnames, and the search patterns tried.
5. Final re-audit will still show "mechanical mismatches" for the false-positive cases — verify each by spot-checking the target footnote content. Do not retarget these.

Gate

• Consensus retargets applied (intersection of --gemini and --batch)
• Manual remap built and applied for disagreements
• Each surviving "mismatch" flag spot-checked against actual target content
• No supra/infra reference points at an unrelated footnote
• SUPRA_UNRESOLVED.md written (or empty if all resolved)

What Gets Converted

What Is NOT Converted (Phase 2 — Future)

• Part/Section references (supra Section I.A., infra Part III)
• These require a heading-to-bookmark mapping strategy since heading numbering is partially auto-generated

Gate: Exit Cross-References

• Dry run reviewed — cross-reference map is correct
• NOTEREF fields created for all supra/infra note references
• Target audit passed — audit_crossref_targets.py run + Step 4 manual verification complete; no NOTEREF points at an unrelated footnote
• Backup DOCX exists
• Word field update (Ctrl+A, F9) confirms correct numbers

Workflow Complete

Present final summary to user:

• Total formatting corrections applied (from Phase 4)
• Total URLs archived (from Phase 6)
• Total NOTEREF fields created (from Phase 7)
• Final DOCX file path