Paper A v3: full rewrite for IEEE Access with three-method convergence

Major changes from v2:

Terminology:
- "digitally replicated" -> "non-hand-signed" throughout (per partner v3
  feedback and to avoid implicit accusation)
- "Firm A near-universal non-hand-signing" -> "replication-dominated"
  (per interview nuance: most but not all Firm A partners use replication)

Target journal: IEEE TAI -> IEEE Access (per NCKU CSIE list)

New methodological sections (III.G-III.L + IV.D-IV.G):
- Three convergent threshold methods (KDE antimode + Hartigan dip test /
  Burgstahler-Dichev McCrary / EM-fitted Beta mixture + logit-GMM
  robustness check)
- Explicit unit-of-analysis discussion (signature vs accountant)
- Accountant-level 2D Gaussian mixture (BIC-best K=3 found empirically)
- Pixel-identity validation anchor (no manual annotation needed)
- Low-similarity negative anchor + Firm A replication-dominated anchor

New empirical findings integrated:
- Firm A signature cosine UNIMODAL (dip p=0.17) - long left tail = minority
  hand-signers
- Full-sample cosine MULTIMODAL but not cleanly bimodal (BIC prefers 3-comp
  mixture) - signature-level is continuous quality spectrum
- Accountant-level mixture trimodal (C1 Deloitte-heavy 139/141,
  C2 other Big-4, C3 smaller firms). 2-comp crossings cos=0.945, dh=8.10
- Pixel-identity anchor (310 pairs) gives perfect recall at all cosine
  thresholds
- Firm A anchor rates: cos>0.95=92.5%, dual-rule cos>0.95 AND dh<=8=89.95%

New discussion section V.B: "Continuous-quality spectrum vs discrete-
behavior regimes" - the core interpretive contribution of v3.

References added: Hartigan & Hartigan 1985, Burgstahler & Dichev 1997,
McCrary 2008, Dempster-Laird-Rubin 1977, White 1982 (refs 37-41).

export_v3.py builds Paper_A_IEEE_Access_Draft_v3.docx (462 KB, +40% vs v2
from expanded methodology + results sections).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

paper/paper_a_impact_statement_v3.md

@@ -0,0 +1,9 @@
+# Impact Statement
+
+<!-- 100-150 words. Non-specialist readable. No jargon. Specific, not vague. -->
+
+Auditor signatures on financial reports are a key safeguard of corporate accountability.
+When the signature on an audit report is produced by reproducing a stored image instead of by the partner's own hand---whether through an administrative stamping workflow or a firm-level electronic signing system---this safeguard is weakened, yet detecting the practice through manual inspection is infeasible at the scale of modern financial markets.
+We developed an artificial intelligence system that automatically extracts and analyzes signatures from over 90,000 audit reports spanning a decade of filings by publicly listed companies in Taiwan.
+By combining deep-learning visual features with perceptual hashing and three statistically independent threshold-selection methods, the system distinguishes genuinely hand-signed signatures from reproduced ones and quantifies how this practice varies across firms and over time.
+After further validation, the technology could support financial regulators in automating signature-authenticity screening at national scale.