feat(governance): implement publication eligibility gates, proportional dynamic redistribution, CI workflow, and 20/20 QA suite

scripts/audit_weights.py

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+"""
+scripts/audit_weights.py
+Audits dynamic weight allocation, platform presence tiers, and high-rank evidence.
+"""
+from pathlib import Path
+import pandas as pd
+import numpy as np
+
+DATASET_PATH = Path("data/hiffi_multiplatform_500.csv")
+
+if not DATASET_PATH.exists():
+    raise FileNotFoundError(f"Master cohort not found at {DATASET_PATH}")
+
+df = pd.read_csv(DATASET_PATH)
+
+# Explicitly define platform presence (YouTube, Spotify presence, Instagram presence)
+df["has_youtube"] = df["production_hiffi_score"].notna()
+df["has_spotify"] = df["spotify_monthly_listeners"].notna()
+df["has_instagram"] = df["instagram_followers"].notna()
+
+df["verified_platform_count"] = (
+    df["has_youtube"].astype(int) +
+    df["has_spotify"].astype(int) +
+    df["has_instagram"].astype(int)
+)
+
+print("=" * 70)
+print("1. VERIFIED PLATFORM PRESENCE TIERS")
+print("=" * 70)
+tier_summary = df.groupby("verified_platform_count")["hiffi_xp_score"].agg(
+    count="count",
+    mean="mean",
+    std="std",
+    min="min",
+    max="max"
+).round(2)
+print(tier_summary)
+
+print("\n" + "=" * 70)
+print("2. TOP 50 PLATFORM EVIDENCE DISTRIBUTION")
+print("=" * 70)
+top_50 = df[df["hiffi_xp_rank"] <= 50]
+top_50_dist = top_50["verified_platform_count"].value_counts().sort_index(ascending=False)
+for tier, count in top_50_dist.items():
+    print(f"Tier {tier} Platforms Verified: {count:2d} artists ({count / len(top_50) * 100:4.1f}%)")
+
+print("\n" + "=" * 70)
+print("3. TOP 10 PLATFORM EVIDENCE DISTRIBUTION")
+print("=" * 70)
+top_10 = df[df["hiffi_xp_rank"] <= 10]
+top_10_dist = top_10["verified_platform_count"].value_counts().sort_index(ascending=False)
+for tier, count in top_10_dist.items():
+    print(f"Tier {tier} Platforms Verified: {count:2d} artists ({count / len(top_10) * 100:4.1f}%)")
+
+print("\n" + "=" * 70)
+print("4. RANK DELTA EXTREMES & AUDIT TRAIL")
+print("=" * 70)
+cols = ["name", "production_hiffi_rank", "hiffi_xp_rank", "rank_delta", "verified_platform_count"]
+top_gainers = df.sort_values(by="rank_delta", ascending=False).head(3)[cols]
+top_fallers = df.sort_values(by="rank_delta", ascending=True).head(3)[cols]
+
+print("Top 3 Gainers:")
+print(top_gainers.to_string(index=False))
+print("\nTop 3 Fallers:")
+print(top_fallers.to_string(index=False))
+print("=" * 70)