Paper A v3.4: resolve codex round-3 major-revision blockers

Three blockers from codex gpt-5.4 round-3 review (codex_review_gpt54_v3_3.md):

B1 Classifier vs three-method threshold mismatch
  - Methodology III-L rewritten to make explicit that the per-signature
    classifier and the accountant-level three-method convergence operate
    at different units (signature vs accountant) and are complementary
    rather than substitutable.
  - Add Results IV-G.3 + Table XII operational-threshold sensitivity:
    cos>0.95 vs cos>0.945 shifts dual-rule capture by 1.19 pp on whole
    Firm A; ~5% of signatures flip at the Uncertain/Moderate boundary.

B2 Held-out validation false "within Wilson CI" claim
  - Script 24 recomputes both calibration-fold and held-out-fold rates
    with Wilson 95% CIs and a two-proportion z-test on each rule.
  - Table XI replaced with the proper fold-vs-fold comparison; prose
    in Results IV-G.2 and Discussion V-C corrected: extreme rules agree
    across folds (p>0.7); operational rules in the 85-95% band differ
    by 1-5 pp due to within-Firm-A heterogeneity (random 30% sample
    contained more high-replication C1 accountants), not generalization
    failure.

B3 Interview evidence reframed as practitioner knowledge
  - The Firm A "interviews" referenced throughout v3.3 are private,
    informal professional conversations, not structured research
    interviews. Reframed accordingly: all "interview*" references in
    abstract / intro / methodology / results / discussion / conclusion
    are replaced with "domain knowledge / industry-practice knowledge".
  - This avoids overclaiming methodological formality and removes the
    human-subjects research framing that triggered the ethics-statement
    requirement.
  - Section III-H four-pillar Firm A validation now stands on visual
    inspection, signature-level statistics, accountant-level GMM, and
    the three Section IV-H analyses, with practitioner knowledge as
    background context only.
  - New Section III-M ("Data Source and Firm Anonymization") covers
    MOPS public-data provenance, Firm A/B/C/D pseudonymization, and
    conflict-of-interest declaration.

Add signature_analysis/24_validation_recalibration.py for the recomputed
calib-vs-held-out z-tests and the classifier sensitivity analysis;
output in reports/validation_recalibration/.

Pending (not in this commit): abstract length (368 -> 250 words),
Impact Statement removal, BD/McCrary sensitivity reporting, full
reproducibility appendix, references cleanup.

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

paper/paper_a_introduction_v3.md

@@ -48,11 +48,10 @@ Perceptual hashing (specifically, difference hashing) encodes structural-level i
 By requiring convergent evidence from both descriptors, we can differentiate *style consistency* (high cosine but divergent dHash) from *image reproduction* (high cosine with low dHash), resolving an ambiguity that neither descriptor can address alone.
 
 A second distinctive feature is our framing of the calibration reference.
-One major Big-4 accounting firm in Taiwan (hereafter "Firm A") is widely recognized within the audit profession as making substantial use of non-hand-signing.
-Structured interviews with multiple Firm A partners confirm that *most* certifying partners produce their audit-report signatures by reproducing a stored image while not excluding that a *minority* may continue to hand-sign some reports.
+One major Big-4 accounting firm in Taiwan (hereafter "Firm A") is widely recognized within the audit profession as making substantial use of non-hand-signing for the majority of its certifying partners, while not ruling out that a minority may continue to hand-sign some reports.
 We therefore treat Firm A as a *replication-dominated* calibration reference rather than a pure positive class.
 This framing is important because the statistical signature of a replication-dominated population is visible in our data: Firm A's per-signature cosine distribution is unimodal with a long left tail, 92.5% of Firm A signatures exceed cosine 0.95 but 7.5% fall below, and 32 of the 171 Firm A CPAs with enough signatures to enter our accountant-level analysis (of 180 Firm A CPAs in total) cluster into an accountant-level "middle band" rather than the high-replication mode.
-Adopting the replication-dominated framing---rather than a near-universal framing that would have to absorb these residuals as noise---ensures internal coherence between the interview evidence and the statistical results.
+Adopting the replication-dominated framing---rather than a near-universal framing that would have to absorb these residuals as noise---ensures internal coherence among the visual-inspection evidence, the signature-level statistics, and the accountant-level mixture.
 
 A third distinctive feature is our unit-of-analysis treatment.
 Our three-method framework reveals an informative asymmetry between the signature level and the accountant level: per-signature similarity forms a continuous quality spectrum for which no two-mechanism mixture provides a good fit, whereas per-accountant aggregates are clustered into three recognizable groups (BIC-best $K = 3$).