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Phase 3 close-out: Script 42 + §IV draft v2 (Table XV filled)

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Script 42 tabulates the §III-L five-way per-signature classifier
output on the Big-4 sub-corpus (n=150,442 signatures classified)
and aggregates to document-level (n=75,233 unique PDFs) under
the worst-case rule.

Per-signature five-way overall (Table XV):

  HC  74,593  49.58%  high-confidence non-hand-signed
  MC  39,817  26.47%  moderate-confidence non-hand-signed
  HSC    314   0.21%  high style consistency
  UN  35,480  23.58%  uncertain
  LH     238   0.16%  likely hand-signed

Per-firm five-way (% within firm):

  Firm A (Deloitte)  HC 81.70%, MC 10.76%, UN 7.42%
  Firm B (KPMG)      HC 34.56%, MC 35.88%, UN 29.09%
  Firm C (PwC)       HC 23.75%, MC 41.44%, UN 34.21%
  Firm D (EY)        HC 24.51%, MC 29.33%, UN 45.65%

Document-level (Table XV-B, NEW):

  HC  46,857  62.28%
  MC  19,667  26.14%
  HSC    167   0.22%
  UN   8,524  11.33%
  LH      18   0.02%
  Total 75,233 unique Big-4 PDFs (single-firm 74,854; mixed-firm 379)

§IV v2 changes vs v1:
  - Table XV populated with Script 42 counts
  - Table XV-B (NEW): document-level worst-case counts
  - Per-firm five-way breakdown (% within firm) added
  - Per-firm document-level breakdown added
  - Document-level paragraph in §IV-J updated to reference Table XV-B
  - Phase 3 close-out checklist: item 1 (Table XV TBD) and item 4
    (document-level counts) marked RESOLVED; remaining items reduced
    from 5 to 3 (renumbering, content audit, codex open-questions)

The per-firm pattern is consistent with the §III-K Spearman-and-
cluster ordering: Firm A's signatures concentrate in HC (81.7%),
the three non-Firm-A firms have markedly lower HC and substantially
higher Uncertain rates (29-46%), with Firm D having the highest
Uncertain rate of the Big-4 -- consistent with the reverse-anchor
score (§III-K Score 2) ranking Firm D fractionally above Firm C in
the hand-leaning direction.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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gbanyan
2026-05-12 16:45:22 +08:00
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paper/v4/paper_a_results_v4_section_iv.md
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# Section IV. Results — v4.0 Draft v1 # Section IV. Results — v4.0 Draft v2
> **Draft note (2026-05-12).** This file replaces the §IV-A through §IV-H block of `paper/paper_a_results_v3.md` (v3.20.0) with the Big-4 reframed structure; Section IV expands from 8 sub-sections in v3.20.0 to 12 sub-sections in v4.0 to mirror the §III-G..L lineage. Tables IV–XVIII numbering is **provisional** in this draft and finalised in Phase 3 close-out per codex round-22 open question 3. Empirical anchors trace to Scripts 32–41 on branch `paper-a-v4-big4`; the §III provenance table covers the methodology-side citations and §IV adds new tables for the v4.0-specific results. > **Draft note (2026-05-12, v2).** This file replaces the §IV-A through §IV-H block of `paper/paper_a_results_v3.md` (v3.20.0) with the Big-4 reframed structure; Section IV expands from 8 sub-sections in v3.20.0 to 12 sub-sections in v4.0 (A through L) to mirror the §III-G..L lineage. **v2** fills Table XV (and adds Table XV-B for document-level counts) using Script 42's per-signature five-way categorisation on the Big-4 sub-corpus, closing the only TBD that v1 carried. Tables IV–XVIII numbering remains provisional and is finalised in Phase 3 close-out. Empirical anchors trace to Scripts 32–42 on branch `paper-a-v4-big4`; the §III provenance table covers the methodology-side citations and §IV adds new tables for the v4.0-specific results.
## A. Experimental Setup ## A. Experimental Setup
@@ -160,11 +160,55 @@ The signature-level inter-CPA negative-anchor FAR analysis (~50,000 random pairs
This section reports the §III-L five-way per-signature + document-level worst-case classifier output on the Big-4 sub-corpus. The five-way category definitions are inherited unchanged from v3.20.0 §III-K (now §III-L); see §III-L for the cosine and dHash cuts. This section reports the §III-L five-way per-signature + document-level worst-case classifier output on the Big-4 sub-corpus. The five-way category definitions are inherited unchanged from v3.20.0 §III-K (now §III-L); see §III-L for the cosine and dHash cuts.
**Table XV (revised: five-way per-signature category counts, Big-4 only, $n = 150{,}442$).** **Table XV (revised: five-way per-signature category counts, Big-4 only, $n = 150{,}442$ classified).**
We adopt the v3.20.0 Tables IX / XI / XII methodology for the per-signature category counts and re-compute on the Big-4 subset for v4.0; the resulting proportions are reported in this table when the Phase 3 tabulation script is wired to consume the existing per-signature category-assignment output. *[Phase 3 close-out task: regenerate per-signature category counts on Big-4 subset by adapting the v3.x classifier output. Numbers held in v4.0 v1 draft as TBD; the inherited v3.x signature-level rule does not change, only the Big-4 scope of the population over which it is tabulated.]* | Category | Long name | $n$ signatures | % of classified |
|---|---|---|---|
| HC | High-confidence non-hand-signed | 74,593 | 49.58% |
| MC | Moderate-confidence non-hand-signed | 39,817 | 26.47% |
| HSC | High style consistency | 314 | 0.21% |
| UN | Uncertain | 35,480 | 23.58% |
| LH | Likely hand-signed | 238 | 0.16% |
The five-way **moderate-confidence non-hand-signed** band (cos $> 0.95$ AND $5 < \text{dHash} \leq 15$) inherits its v3.x calibration; it is **not separately validated by Scripts 38–40**, which evaluated only the binary high-confidence rule (cos $> 0.95$ AND dHash $\leq 5$). v4.0 does not re-derive the moderate-band cuts on the Big-4 subset; we note this inheritance status explicitly so the reader can locate the v3.x Tables IX / XI / XII calibration evidence (carried into v4.0 by reference) without expecting v4.0-spike-script confirmation of the moderate-band specifics. (Source: Script 42; 11 of 150,453 loaded Big-4 signatures lacked one or both descriptors and were excluded.)
**Per-firm five-way breakdown (% within firm).**
| Firm | HC | MC | HSC | UN | LH | total signatures |
|---|---|---|---|---|---|---|
| Firm A (Deloitte) | 81.70% | 10.76% | 0.05% | 7.42% | 0.07% | 60,448 |
| Firm B (KPMG) | 34.56% | 35.88% | 0.29% | 29.09% | 0.18% | 34,248 |
| Firm C (PwC) | 23.75% | 41.44% | 0.38% | 34.21% | 0.22% | 38,613 |
| Firm D (EY) | 24.51% | 29.33% | 0.22% | 45.65% | 0.29% | 17,133 |
(Source: Script 42 per-firm cross-tab.) The per-firm pattern aligns with the K=3 cluster cross-tab of Table XVI: Firm A is concentrated in the HC band (81.70% of its signatures), consistent with its 82.46% C3-replicated concentration at the accountant level; the three non-Firm-A Big-4 firms have markedly lower HC rates and substantially higher Uncertain rates, with Firm D having the highest Uncertain rate (45.65%) — consistent with §III-K Score 2 (reverse-anchor cosine percentile) ranking Firm D fractionally above Firm C in the hand-leaning direction.
**Document-level worst-case aggregation.** Each audit report typically carries two certifying-CPA signatures. We aggregate signature-level outcomes to document-level labels using the v3.20.0 worst-case rule (HC > MC > HSC > UN > LH; §III-L). v4.0 does not change this aggregation rule; only the population over which it is computed changes (Big-4 subset).
**Table XV-B (NEW: document-level worst-case category counts, Big-4 only, $n = 75{,}233$ unique PDFs).**
| Category | Long name | $n$ documents | % |
|---|---|---|---|
| HC | High-confidence non-hand-signed | 46,857 | 62.28% |
| MC | Moderate-confidence non-hand-signed | 19,667 | 26.14% |
| HSC | High style consistency | 167 | 0.22% |
| UN | Uncertain | 8,524 | 11.33% |
| LH | Likely hand-signed | 18 | 0.02% |
(Source: Script 42 document-level table; 379 of 75,233 PDFs carried signatures from more than one Big-4 firm and are reported in the single-firm-PDF per-firm breakdown of the script CSV but pooled into the overall counts here.)
**Per-firm document-level breakdown (single-firm PDFs only).**
| Firm | HC | MC | HSC | UN | LH | total docs |
|---|---|---|---|---|---|---|
| Firm A (Deloitte) | 27,600 | 1,857 | 7 | 758 | 4 | 30,226 |
| Firm B (KPMG) | 8,783 | 6,079 | 57 | 2,202 | 6 | 17,127 |
| Firm C (PwC) | 7,281 | 8,660 | 77 | 3,099 | 5 | 19,122 |
| Firm D (EY) | 3,100 | 2,838 | 22 | 2,416 | 3 | 8,379 |
(Source: Script 42; mixed-firm PDFs $n = 379$ excluded from the per-firm rows but included in the overall counts above.)
The five-way **moderate-confidence non-hand-signed** band (cos $> 0.95$ AND $5 < \text{dHash} \leq 15$) inherits its v3.x calibration; it is **not separately validated by Scripts 38–40**, which evaluated only the binary high-confidence rule (cos $> 0.95$ AND dHash $\leq 5$). v4.0 does not re-derive the moderate-band cuts on the Big-4 subset; we note this inheritance status explicitly so the reader can locate the v3.x Tables IX / XI / XII calibration evidence (carried into v4.0 by reference) without expecting v4.0-spike-script confirmation of the moderate-band specifics. The Table XV per-firm MC proportions (10.76% / 35.88% / 41.44% / 29.33% across Firms A through D) report the inherited rule's output on the Big-4 subset; the relative ordering of the non-Firm-A firms on MC is consistent with the §III-K Spearman convergence on the per-CPA hand-leaning ranking.
**Table XVI (NEW: firm × K=3 cluster cross-tabulation, Big-4 only).** **Table XVI (NEW: firm × K=3 cluster cross-tabulation, Big-4 only).**
@@ -177,7 +221,7 @@ The five-way **moderate-confidence non-hand-signed** band (cos $> 0.95$ AND $5 <
(Source: Script 35.) The cross-tab is the accountant-level descriptive output of the K=3 mixture (§III-J / §IV-E). It is reported here as a complement to the five-way per-signature classifier (Table XV), not as an operational classifier output. Reading: Firm A's CPAs are concentrated in the C3 replicated component (no Firm A CPAs in C1); Firm C has the highest hand-leaning concentration of the Big-4 (C1 fraction $23.5\%$); Firms B and D sit between A and C on the K=3 hard-label ordering, broadly consistent with the per-firm Spearman ordering of Table X (with the within-Big-4-non-A reverse-anchor disagreement noted there). (Source: Script 35.) The cross-tab is the accountant-level descriptive output of the K=3 mixture (§III-J / §IV-E). It is reported here as a complement to the five-way per-signature classifier (Table XV), not as an operational classifier output. Reading: Firm A's CPAs are concentrated in the C3 replicated component (no Firm A CPAs in C1); Firm C has the highest hand-leaning concentration of the Big-4 (C1 fraction $23.5\%$); Firms B and D sit between A and C on the K=3 hard-label ordering, broadly consistent with the per-firm Spearman ordering of Table X (with the within-Big-4-non-A reverse-anchor disagreement noted there).
**Document-level worst-case aggregation.** Each audit report typically carries two certifying-CPA signatures. Document-level outputs use the v3.20.0 worst-case rule (§III-L; v3.20.0 §III-K); v4.0 does not change this aggregation. Document-level proportions on the Big-4 subset are reported when the Phase 3 tabulation script is wired (see Table XV TBD note). **Document-level worst-case aggregation outputs are reported in Table XV-B above.**
## K. Full-Dataset Robustness (light scope) ## K. Full-Dataset Robustness (light scope)
@@ -215,8 +259,7 @@ The feature-backbone ablation (Table XVIII in v3.20.0; backbone replacement of R
The following items are flagged for resolution before §IV is sent for codex round 23 / partner Jimmy review: The following items are flagged for resolution before §IV is sent for codex round 23 / partner Jimmy review:
1. **Table XV per-signature category counts on Big-4 subset.** The five-way classifier's per-signature counts on the Big-4 subset need to be re-tabulated by adapting the v3.x category-assignment script. Numbers are TBD in v1; the inherited cosine/dHash cuts do not change. 1. **Table XV per-signature category counts** — RESOLVED (v2 of §IV draft, Script 42 output). Per-signature, per-firm, document-level, and per-firm-document tables now populated.
2. **Table renumbering finalisation.** The provisional Tables IV–XVIII numbering should be confirmed once §IV is read end-to-end; some v3.x table positions (e.g., capture-rate tables Tables IX, XI, XII) are kept by reference rather than reproduced as v4.0-numbered tables. 2. **Table renumbering finalisation.** The provisional Tables IV–XVIII numbering (with Table XV-B added in v2) should be confirmed once §IV is read end-to-end and §III–§IV cross-references are traced; some v3.x table positions (e.g., capture-rate tables Tables IX, XI, XII) are kept by reference rather than reproduced as v4.0-numbered tables.
3. **§IV-A to §IV-C content audit.** Verify that the inherited prose for Experimental Setup, Detection Performance, and All-Pairs analysis remains accurate after the §III-G scope change to Big-4 primary. 3. **§IV-A to §IV-C content audit.** Verify that the inherited prose for Experimental Setup, Detection Performance, and All-Pairs analysis remains accurate after the §III-G scope change to Big-4 primary.
4. **Document-level worst-case aggregation counts.** Companion to item 1; the Big-4 subset document-level proportions need to be regenerated in the same Phase 3 close-out tabulation pass. 4. **Open question carry-over from §III v3.** Codex round-22 open questions on five-way moderate-band validation, firm anonymisation policy, and §IV table numbering are addressed in this v2: (a) five-way moderate band documented as inherited from v3.x in §IV-J with Big-4 per-firm proportions reported descriptively (Table XV); (b) firm anonymisation maintained throughout §IV (Firm A–D used consistently); (c) §IV table numbering set provisionally and to be finalised at Phase 3 close-out.
5. **Open question carry-over from §III v3.** Codex round-22 open questions on five-way moderate-band validation, firm anonymisation policy, and §IV table numbering are now addressed: (a) five-way moderate band documented as inherited from v3.x in §IV-J; (b) firm anonymisation maintained throughout §IV (Firm A–D used consistently); (c) §IV table numbering set provisionally and to be finalised at Phase 3 close-out.
signature_analysis/42_v4_five_way_categorisation.py
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#!/usr/bin/env python3
"""
Script 42: Five-Way Per-Signature Categorisation on Big-4 (Table XV fill)
==========================================================================
Phase 3 close-out. Tabulates the §III-L five-way per-signature
classifier output on the Big-4 sub-corpus and aggregates to
document-level (per-PDF) labels under the worst-case rule.
Five-way rule (inherited from v3.20.0 §III-K, retained as v4 §III-L):
cos > 0.95 AND dHash_indep <= 5 -> HC High-confidence non-hand-signed
cos > 0.95 AND 5 < dHash <= 15 -> MC Moderate-confidence non-hand-signed
cos > 0.95 AND dHash > 15 -> HSC High style consistency
0.837 < cos <= 0.95 -> UN Uncertain
cos <= 0.837 -> LH Likely hand-signed
Document-level worst-case rule (one PDF can carry up to 2 certifying-
CPA signatures; the document inherits the most-replication-consistent
signature label among the signatures present):
HC > MC > HSC > UN > LH
Output:
reports/v4_big4/five_way_categorisation/
per_signature_counts.csv
per_firm_category_crosstab.csv
per_document_counts.csv
five_way_results.json
five_way_report.md
"""
import sqlite3
import csv
import json
import numpy as np
from pathlib import Path
from datetime import datetime
DB = '/Volumes/NV2/PDF-Processing/signature-analysis/signature_analysis.db'
OUT = Path('/Volumes/NV2/PDF-Processing/signature-analysis/reports/'
'v4_big4/five_way_categorisation')
OUT.mkdir(parents=True, exist_ok=True)
BIG4 = ('勤業眾信聯合', '安侯建業聯合', '資誠聯合', '安永聯合')
LABEL = {'勤業眾信聯合': 'Firm A (Deloitte)',
'安侯建業聯合': 'Firm B (KPMG)',
'資誠聯合': 'Firm C (PwC)',
'安永聯合': 'Firm D (EY)'}
COS_HIGH = 0.95
COS_LOW = 0.837
DH_HIGH = 5
DH_MOD = 15
# Worst-case priority (HC most-replication-consistent, LH most hand-signed)
PRIORITY = {'HC': 0, 'MC': 1, 'HSC': 2, 'UN': 3, 'LH': 4}
CATEGORIES = ['HC', 'MC', 'HSC', 'UN', 'LH']
CAT_LONG = {
'HC': 'High-confidence non-hand-signed',
'MC': 'Moderate-confidence non-hand-signed',
'HSC': 'High style consistency',
'UN': 'Uncertain',
'LH': 'Likely hand-signed',
}
def classify(cos, dh):
if cos is None:
return None # cannot classify
if cos > COS_HIGH:
if dh is None:
return None # require dh for HC/MC/HSC distinction
if dh <= DH_HIGH:
return 'HC'
if dh <= DH_MOD:
return 'MC'
return 'HSC'
if cos > COS_LOW:
return 'UN'
return 'LH'
def load_big4_signatures():
conn = sqlite3.connect(DB)
cur = conn.cursor()
cur.execute('''
SELECT s.signature_id, s.source_pdf, s.assigned_accountant, a.firm,
s.max_similarity_to_same_accountant,
s.min_dhash_independent
FROM signatures s
JOIN accountants a ON s.assigned_accountant = a.name
WHERE s.assigned_accountant IS NOT NULL
AND a.firm IN (?, ?, ?, ?)
''', BIG4)
rows = cur.fetchall()
conn.close()
return rows
def main():
print('=' * 72)
print('Script 42: Five-Way Per-Signature Categorisation (Big-4)')
print('=' * 72)
rows = load_big4_signatures()
print(f'\nN Big-4 signatures (loaded, including missing-descriptor): '
f'{len(rows):,}')
# Per-signature classification
per_sig = []
n_unclassified = 0
for r in rows:
sig_id, pdf, cpa, firm, cos, dh = r
cos_f = None if cos is None else float(cos)
dh_f = None if dh is None else float(dh)
cat = classify(cos_f, dh_f)
if cat is None:
n_unclassified += 1
continue
per_sig.append({
'sig_id': sig_id, 'pdf': pdf, 'cpa': cpa, 'firm': firm,
'cos': cos_f, 'dh': dh_f, 'cat': cat,
})
n_classified = len(per_sig)
print(f' Classified: {n_classified:,}')
print(f' Unclassified (missing cos/dh): {n_unclassified:,}')
# Overall per-signature counts
overall = {c: 0 for c in CATEGORIES}
for s in per_sig:
overall[s['cat']] += 1
print('\n=== Overall per-signature counts (Big-4 classified) ===')
print(f' {"cat":<5} {"long":<40} {"n":>8} {"%":>7}')
for c in CATEGORIES:
n = overall[c]
pct = 100 * n / n_classified if n_classified else 0.0
print(f' {c:<5} {CAT_LONG[c]:<40} {n:>8,} {pct:>6.2f}%')
# Per-firm × category cross-tab
by_firm = {f: {c: 0 for c in CATEGORIES} for f in BIG4}
for s in per_sig:
by_firm[s['firm']][s['cat']] += 1
print('\n=== Per-firm × category cross-tab (counts) ===')
print(f' {"Firm":<22} ' + ' '.join(f'{c:>8}' for c in CATEGORIES)
+ f' {"total":>8}')
for f in BIG4:
cells = [by_firm[f][c] for c in CATEGORIES]
total = sum(cells)
print(f' {LABEL[f]:<22} '
+ ' '.join(f'{n:>8,}' for n in cells)
+ f' {total:>8,}')
print('\n=== Per-firm × category cross-tab (% within firm) ===')
for f in BIG4:
cells = [by_firm[f][c] for c in CATEGORIES]
total = sum(cells) or 1
print(f' {LABEL[f]:<22} '
+ ' '.join(f'{100*n/total:>7.2f}%' for n in cells)
+ f' total {total:>6,}')
# Document-level (per-PDF) aggregation under worst-case rule
by_pdf = {}
for s in per_sig:
pdf = s['pdf']
if pdf not in by_pdf:
by_pdf[pdf] = {'firm_set': set(), 'best_cat': None,
'best_priority': 99, 'n_sigs': 0}
bp = by_pdf[pdf]
bp['n_sigs'] += 1
bp['firm_set'].add(s['firm'])
prio = PRIORITY[s['cat']]
if prio < bp['best_priority']:
bp['best_priority'] = prio
bp['best_cat'] = s['cat']
n_docs = len(by_pdf)
docs_overall = {c: 0 for c in CATEGORIES}
for pdf, bp in by_pdf.items():
docs_overall[bp['best_cat']] += 1
print(f'\n=== Document-level (n={n_docs:,} unique Big-4 PDFs) ===')
print(f' {"cat":<5} {"long":<40} {"n_docs":>8} {"%":>7}')
for c in CATEGORIES:
n = docs_overall[c]
pct = 100 * n / n_docs if n_docs else 0.0
print(f' {c:<5} {CAT_LONG[c]:<40} {n:>8,} {pct:>6.2f}%')
# Document-level by firm (use first firm in the set; PDFs with mixed
# firm signatures are rare and reported separately)
docs_by_firm = {f: {c: 0 for c in CATEGORIES} for f in BIG4}
docs_mixed_firm = {c: 0 for c in CATEGORIES}
n_mixed_firm = 0
for pdf, bp in by_pdf.items():
if len(bp['firm_set']) == 1:
firm = next(iter(bp['firm_set']))
if firm in BIG4:
docs_by_firm[firm][bp['best_cat']] += 1
else:
n_mixed_firm += 1
docs_mixed_firm[bp['best_cat']] += 1
print(f'\n=== Document-level per-firm (single-firm PDFs only; '
f'mixed-firm = {n_mixed_firm}) ===')
print(f' {"Firm":<22} ' + ' '.join(f'{c:>8}' for c in CATEGORIES)
+ f' {"total":>8}')
for f in BIG4:
cells = [docs_by_firm[f][c] for c in CATEGORIES]
total = sum(cells)
print(f' {LABEL[f]:<22} '
+ ' '.join(f'{n:>8,}' for n in cells)
+ f' {total:>8,}')
# Persist CSVs
sig_csv = OUT / 'per_signature_counts.csv'
with open(sig_csv, 'w', newline='', encoding='utf-8') as f:
w = csv.writer(f)
w.writerow(['cat', 'long_name', 'n', 'pct_of_classified'])
for c in CATEGORIES:
w.writerow([c, CAT_LONG[c], overall[c],
f'{100*overall[c]/n_classified:.2f}'
if n_classified else '0'])
firm_csv = OUT / 'per_firm_category_crosstab.csv'
with open(firm_csv, 'w', newline='', encoding='utf-8') as f:
w = csv.writer(f)
w.writerow(['firm', 'firm_label'] + CATEGORIES + ['total']
+ [f'{c}_pct' for c in CATEGORIES])
for fk in BIG4:
cells = [by_firm[fk][c] for c in CATEGORIES]
total = sum(cells) or 1
w.writerow([fk, LABEL[fk]] + cells + [sum(cells)]
+ [f'{100*n/total:.2f}' for n in cells])
doc_csv = OUT / 'per_document_counts.csv'
with open(doc_csv, 'w', newline='', encoding='utf-8') as f:
w = csv.writer(f)
w.writerow(['scope', 'cat', 'long_name', 'n', 'pct'])
for c in CATEGORIES:
w.writerow(['overall', c, CAT_LONG[c], docs_overall[c],
f'{100*docs_overall[c]/n_docs:.2f}' if n_docs
else '0'])
for fk in BIG4:
firm_total = sum(docs_by_firm[fk][c] for c in CATEGORIES) or 1
for c in CATEGORIES:
w.writerow([LABEL[fk], c, CAT_LONG[c],
docs_by_firm[fk][c],
f'{100*docs_by_firm[fk][c]/firm_total:.2f}'])
for c in CATEGORIES:
w.writerow(['mixed_firm', c, CAT_LONG[c], docs_mixed_firm[c],
f'{100*docs_mixed_firm[c]/n_mixed_firm:.2f}'
if n_mixed_firm else '0'])
payload = {
'generated_at': datetime.now().isoformat(),
'rule': {
'cos_high': COS_HIGH, 'cos_low': COS_LOW,
'dh_high': DH_HIGH, 'dh_mod': DH_MOD,
},
'priority': PRIORITY,
'n_loaded': len(rows),
'n_classified': n_classified,
'n_unclassified': n_unclassified,
'per_signature_overall': {c: overall[c] for c in CATEGORIES},
'per_signature_by_firm': {fk: by_firm[fk] for fk in BIG4},
'document_level': {
'n_docs': n_docs,
'overall': docs_overall,
'by_firm_single_firm_docs_only': {
fk: docs_by_firm[fk] for fk in BIG4
},
'n_mixed_firm_docs': n_mixed_firm,
'mixed_firm_overall': docs_mixed_firm,
},
}
json_path = OUT / 'five_way_results.json'
json_path.write_text(json.dumps(payload, indent=2, ensure_ascii=False),
encoding='utf-8')
print(f'\nJSON: {json_path}')
# Markdown
md = [
'# §IV-J Five-Way Per-Signature Categorisation on Big-4 (Table XV fill)',
f'Generated: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")}',
'',
'## Rule (inherited from v3.20.0 §III-K)',
'',
f'- HC : cos > {COS_HIGH} AND dHash_indep <= {DH_HIGH}',
f'- MC : cos > {COS_HIGH} AND {DH_HIGH} < dHash <= {DH_MOD}',
f'- HSC: cos > {COS_HIGH} AND dHash > {DH_MOD}',
f'- UN : {COS_LOW} < cos <= {COS_HIGH}',
f'- LH : cos <= {COS_LOW}',
'',
'## Sample',
'',
f'- Loaded Big-4 signatures: {len(rows):,}',
f'- Classified (both descriptors available): '
f'{n_classified:,}',
f'- Unclassified (missing cos or dh): {n_unclassified:,}',
'',
'## Per-signature overall counts (Table XV — Big-4 subset)',
'',
'| Category | Long name | $n$ signatures | % of classified |',
'|---|---|---|---|',
]
for c in CATEGORIES:
n = overall[c]
pct = 100 * n / n_classified if n_classified else 0.0
md.append(f'| {c} | {CAT_LONG[c]} | {n:,} | {pct:.2f}% |')
md += ['', '## Per-firm × category cross-tab (counts)', '',
'| Firm | HC | MC | HSC | UN | LH | total |',
'|---|---|---|---|---|---|---|']
for fk in BIG4:
cells = [by_firm[fk][c] for c in CATEGORIES]
total = sum(cells)
md.append(f'| {LABEL[fk]} | '
+ ' | '.join(f'{n:,}' for n in cells)
+ f' | {total:,} |')
md += ['', '## Per-firm × category cross-tab (% within firm)', '',
'| Firm | HC % | MC % | HSC % | UN % | LH % |',
'|---|---|---|---|---|---|']
for fk in BIG4:
cells = [by_firm[fk][c] for c in CATEGORIES]
total = sum(cells) or 1
md.append(f'| {LABEL[fk]} | '
+ ' | '.join(f'{100*n/total:.2f}%' for n in cells)
+ ' |')
md += ['', '## Document-level (worst-case rule, per Big-4 PDF)', '',
f'- N unique Big-4 PDFs: {n_docs:,}',
f'- Mixed-firm PDFs (signatures from >1 Big-4 firm; reported '
f'separately below): {n_mixed_firm:,}',
'',
'| Category | Long name | $n$ documents | % |',
'|---|---|---|---|']
for c in CATEGORIES:
n = docs_overall[c]
pct = 100 * n / n_docs if n_docs else 0.0
md.append(f'| {c} | {CAT_LONG[c]} | {n:,} | {pct:.2f}% |')
md += ['', '## Document-level per-firm (single-firm PDFs only)', '',
'| Firm | HC | MC | HSC | UN | LH | total |',
'|---|---|---|---|---|---|---|']
for fk in BIG4:
cells = [docs_by_firm[fk][c] for c in CATEGORIES]
total = sum(cells)
md.append(f'| {LABEL[fk]} | '
+ ' | '.join(f'{n:,}' for n in cells)
+ f' | {total:,} |')
md += ['', '## Files',
'- `per_signature_counts.csv` -- overall five-way per-signature counts',
'- `per_firm_category_crosstab.csv` -- per-firm cross-tab',
'- `per_document_counts.csv` -- document-level aggregation',
'- `five_way_results.json` -- machine-readable full output',
]
md_path = OUT / 'five_way_report.md'
md_path.write_text('\n'.join(md), encoding='utf-8')
print(f'Report: {md_path}')
if __name__ == '__main__':
main()