A data-driven analysis of temporal correlations between friction events, policy shifts, and capital flows (2015-2026).
New to this project? See Glossary.md
In a rush? See consolidation_pattern_significance.md
- Quick Summary
- Understanding the Statistics
- Key Statistics
- The Convergence Model
- The Three-Layer Model
- Repository Structure
- What's New (v8.5)
- Quick Navigation by Type
- For Researchers
- For Policymakers & Journalists
- Methodology
- Limitations & Disclaimer
- Connected Repositories
- Statistical Validation
This repository documents statistically significant correlations between high-visibility "friction" events (file releases, scandals, media cycles) and institutional compliance events (policy shifts, financial positioning, regulatory changes).
Core Finding: Friction events (document releases, scandals) predict institutional compliance events (policy shifts, financial moves) at a 2-week lag (r = +0.6196, p = 0.0004, n = 30 weeks).
What this means: When high-visibility friction events spike, institutional compliance events follow ~14 days later — suggesting shared exploitation of calendar-driven attention windows.
This research uses Pearson correlation (r) to measure relationships between event types.
| r Value | Interpretation |
|---|---|
| 0.0 | No relationship |
| ±0.1-0.3 | Weak |
| ±0.3-0.5 | Moderate |
| ±0.5-0.7 | Strong |
| ±0.7-1.0 | Very strong |
Our finding (r = +0.6196): When friction events spike, compliance events follow approximately 2 weeks later. This correlation (p = 0.0004) has less than 0.05% probability of occurring by chance.
Why this matters:
- A correlation this strong is reproducible — run the scripts in
Run_Correlations_Yourself/yourself - It suggests a structural pattern, not random coincidence
- Separate analyses (Project Trident, cross-validation) confirm the pattern using independent datasets
Important caveat: Correlation ≠ causation. We document that these events cluster together, not that one causes the other. The claim is structural: the pattern exists and is statistically significant.
| Finding | Value | Status |
|---|---|---|
| Friction → Compliance correlation | r = +0.6196 (2-week lag) | ✅ Verified |
| Statistical significance | p = 0.0004, n = 30 weeks | ✅ Verified |
| Ritual → Policy proximity | 50.7% vs. 19.9% baseline (2.5x) | ✅ Verified |
| Project Trident significance | p = 0.002 (Mann-Whitney U) | ✅ Verified |
| Cross-validation (14-day periodicity) | χ² = 330.62 (p < 0.0001, 2,102 events) | ✅ Verified |
| December 2025 cluster | 108 events in 12-day window | ✅ Verified |
| Dec 22 signal types | 5 (Friction, Geopolitics, Financial, Policy, Cyber) | ✅ Verified |
| Event colocation | Friction dates attract 20–42x more compliance than random dates | ✅ Verified |
| January 2026 signal peaks | 3 peaks (Jan 3-9, Jan 20-22, Jan 27-31), 1 trough (Jan 10-16) | ✅ Verified |
| January 2026 event density | 34 events: 12 friction, 19 compliance, 3 anchors | ✅ Verified |
| Feb 1–19 compliance window | 9 compliance events to 6 friction events in 19 days | ✅ Verified |
Friction (t) → [creates window] → Compliance (t+14 days)
Calendar Anchor (solstice, holiday, fiscal deadline)
↓
┌───────┼───────┐
↓ ↓ ↓
Friction Policy Financial
↓
Lagged Clustering (r = 0.6196, 2-week lag)
The raw data shows friction and compliance events cluster together rather than following a sequential cause-effect pattern. This makes the phenomenon more robust—it doesn't require coordination, just shared exploitation of the same calendar signals.
The Dec 19-23, 2025 window demonstrates convergent clustering:
| Date | Event Type | Events |
|---|---|---|
| Dec 19 | Friction | Epstein Library release (DOJ) |
| Dec 19 | Financial | Bull & Bear sell signal (8.5) |
| Dec 22 | Geopolitics | China EU dairy tariffs (42.7%) |
| Dec 22 | Financial | BlackRock Bitcoin ETF "top theme" |
| Dec 22 | Cyber/Intel | CRINK nation-state threat analysis |
| Dec 22-23 | Peak | 6-8 friction + 9-13 compliance events/day |
| Dec 23 | Infrastructure | Redaction failures exposed (NYT) |
Five independent signal types converged on the same window—not because one caused another, but because all actors respond to the same environmental signals.
This repository is part of a three-layer analytical framework:
| Layer | Repository | Focus | Primary Finding |
|---|---|---|---|
| 1. Attention Capture | This repo | Friction → compliance clustering | r = 0.6196 (2-week lag) |
| 2. Vacuum Creation | DOGE_Global_Effects | Aid cuts → instability | r = 0.42-0.69, 3-12 month lag |
| 3. Alternative Capture | BRICS-NDB-LocalCurrency-DiD | Alternative financial systems | +25.5 pp local currency lending |
Unified Thesis: Domestic chaos consumes attention while foreign policy vacuums emerge, which alternative systems then capture.
The_Regulated_Friction_Project/
│
├── README.md # This file
├── Report.md # Executive summary and findings report
├── CITATION.cff # Citation metadata
├── Glossary.md # Key terminology definitions
├── SOURCES.md # 138 unique sources catalogued across all datasets
├── resistance_indicators.md # Resistance indicator tracking
│
├── Core Analysis
│ ├── Repository_Synthesis.md # Three-layer framework overview
│ ├── Thermostat_Explained.md # Why the mechanism exists
│ ├── Claude's_Analysis.md # AI-assisted interpretation
│ ├── Grok_Analysis.md # Cross-verification with Grok
│ ├── China_State_Media_Null_and_Meanings.md # Null finding: China state media shows no anticipatory signaling
│ └── Case_Study_David_Barnes_Detention.md # Hostage diplomacy as human leverage dimension
│
├── December 2025 Focus
│ ├── CRUCIAL_Synthesis_Dec19_Convergence.md # Dec 19-23 pincer window analysis
│ ├── FINANCIAL_RECEIPT_VERIFICATION.md # Financial event verification
│ └── Main_Characters.md # Cabinet timing analysis
│
├── Policy & Implications
│ ├── How_This_Happened-A_Policy_Breif.md # Regulatory citations, oversight questions
│ ├── Implications.md # China BRI expansion implications
│ ├── 'Transparency'_Timeline.md # Document release history
│ └── Alternate_Mechanisms.md # Alternative explanations considered
│
├── Methodology & Transparency
│ ├── TRANSPARENCY_NOTE_FOR_2026_ANALYSIS.md # Dataset inclusion/exclusion criteria
│ └── VERIFICATION_REPORT_Jan2026.md # Complete independent statistical verification
│
├── 00_Quick_Breakdowns/ # Executive-level summaries
│ ├── About_Me.md # Background on the author
│ └── Copilot_Executive_Synthesis_Feb2026.md # Comprehensive repository synthesis (Feb 2026)
│
├── 01_Levers_and_Frictions/ # Friction events timeline
│ ├── Epstein_Files_timeline.csv
│ └── Epstein_Files_timeline_updated.csv
│
├── 02_Anchors_and_Financials/ # Capital flow data
│ ├── pep_banking_combined.csv
│ └── pep_banking_sentiment.csv
│
├── 03_Master_Framework/ # Primary datasets
│ ├── MASTER_reflexive_control_2015-2025.csv
│ ├── MASTER_timeline_2015-2025_UPDATED.csv
│ └── updated_master_theory.csv
│
├── 04_Testing_and_Counters/ # Validation datasets
│ ├── expanded_historical_backtest.csv
│ └── merged_backtest_counters.csv
│
├── 05_Geopolitical_Vectors/ # Nation-state analysis
│ ├── CRINK_Analysis.md # CRINK axis integration
│ ├── Global_Election_Analysis.md # Allied election patterns
│ ├── Graham_Venezuela_Analysis.md # Graham's 54-day Venezuela escalation
│ ├── Graham_Venezuela_Posts_Timeline.csv # Supporting timeline data
│ ├── January_2026_Parallel_Operations_Timeline.md # Venezuela-Yemen parallel operations
│ ├── Venezuela_Privatization_Amnesty_Stack_Feb2026.md # Venezuela compliance stack
│ └── thermostat_control_data.csv # Nation-state linkage data
│
├── 06_Visualizations/ # Charts and images
│
├── 07_My_Previous_Epstein_Research/ # Pre-project archive (4 PDFs)
│
├── 08_How_It's_Possible/ # Mechanism documentation
│ ├── 08_How_Its_Possible.md # Core mechanism analysis
│ ├── DOJ_Probe_Results.csv
│ ├── Phase_0_Maxwell_Pivot.csv
│ └── pincer_data.csv
│
├── 09_Silicon_Sovereignty/ # Semiconductor, AI & infrastructure
│ ├── SILICON_SOVEREIGNTY_REPORT.md # Core report: compute-as-currency thesis
│ ├── Infrastructure_Consolidation_Pattern_Jan2026.md # Oracle/MGX/PIF consortium analysis
│ ├── CRUCIAL-Cross_Verification_Check.md # Cross-verification with llama2
│ ├── Coalition_Narrative_Map_2015-2025.csv # Media coverage patterns (456 records)
│ ├── REFINED_supercomputer_geopolitics (1).csv # Supercomputer geopolitics (906 records)
│ ├── Regulatory_Map_Data_CLEANED.csv # Policy/compliance events (76 records)
│ └── VOCA_funding_timeline_clean.csv # Victim services funding (667 records)
│
├── 10_Real-Time_Updates_and_Tasks/ # Ongoing analysis and daily tasks
│ ├── README.md
│ ├── 2026_January/ # January 2026 daily updates
│ ├── 2026_February/ # February 2026 daily updates
│ └── Tasks/ # Standing monitoring tasks (14 trackers)
│
├── 11_Protest_Dynamics_and_Funding/ # Protest analysis
│ ├── Protest_Funding_Audit.pdf
│ └── README.md
│
├── 12_The_Media_Firewall/ # Media coverage patterns
│ ├── 1789_Symbolism_Analysis.md # 1789 Capital / Gulf SWF funding network
│ ├── Analyzing Geopolitical and Media Control.pdf
│ └── README.md
│
├── 13_State_and_County_Analysis/ # State-level forensics
│ └── arkansas_infrastructure_forensic_audit.md # Arkansas Act 373/548, PSC analysis
│
├── Statistical Verification
│ ├── Control_Proof/ # Core correlation data
│ │ ├── master_reflexive_correlation_data.csv # Weekly friction/compliance indices
│ │ ├── MASTER_reflexive_control_v2.csv
│ │ ├── reflexive_control_scraped_data.csv
│ │ ├── thermostat_control_data.csv
│ │ └── correlation_results.txt
│ │
│ ├── Run_Correlations_Yourself/ # Independent verification suite
│ │ ├── README.md # Folder guide and correlation reference
│ │ ├── requirements.txt # Python dependencies (pandas, numpy, scipy, statsmodels)
│ │ ├── run_original_analysis.py # Reproduce r = 0.6196, p = 0.0004, Mann-Whitney p = 0.002 (pre-2026 data)
│ │ └── Wrong_Correlations/ # ⚠️ Archived scripts that used wrong datasets or excluded data
│ │ ├── README.md # Explanation of what went wrong
│ │ ├── reproduce_updated_correlation.py # ⚠️ DEPRECATED (used 2025-only datasets — produced inflated r = 0.6685)
│ │ ├── original_correlation_test.py # (used relative paths — was correct)
│ │ ├── reproduce_original_correlation.py # (used hardcoded paths to wrong datasets)
│ │ ├── independent_statistical_verification.py # (used hardcoded paths to wrong datasets)
│ │ ├── run_original_analysis.py # (was correct — archived copy)
│ │ └── DISCREPANCY_ANALYSIS.md # Methodology comparison (still valid)
│ │
│ └── Project_Trident/ # Temporal correlation study
│ ├── PROJECT_TRIDENT_CASE_STUDY.md
│ ├── The_Trident.md # Three-prong mechanism
│ ├── DATASET_REFERENCE.md
│ ├── Veriify_Trident_Analysis.py # Verify ritual timing analysis
│ ├── anchor_events_parsed.csv # 70 anchor events
│ ├── project_trident_final_dossier.csv # 118 dossier entries
│ ├── ritual_events_parsed.csv # 51 ritual events
│ ├── temporal_correlations_analyzed.csv # 338 temporal pairings
│ ├── Best_Data_For_Project_Trident/ # Ritual timing, fund flow datasets (8 files)
│ ├── Claude_Code_Analysis/ # Q1 2026 Privatized Integration research
│ │ ├── Privatized_Integration_Networks_Q1_2026_Synthesis.md # Master document
│ │ ├── Phoenix_Settlement_Portfolio_and_New_Gaza.md # Reference appendix
│ │ ├── LEAD_ANALYST_REVIEW.md # Independent verification
│ │ └── README.md
│ └── Copilot_Opus_4.6_Analysis/ # Lead Researcher statistical verification
│ ├── README.md # Transparency notice, methodology, work log
│ ├── Statistical_Tests/ # 9 runnable Python robustness scripts
│ ├── Findings/ # Active analysis — provenance, backfill guide
│ ├── Verification_Reports/ # Prediction tracker
│ ├── Consolidation_Analysis/ # Cross-domain consolidation assessment
│ ├── FaaS_Signal_Analysis/ # SuperGrok signal verification + January 2026 signal map
│ ├── Influencer_Narrative_Timing/ # Media Firewall narrative timing analysis
│ ├── Archive/ # Previous analysis kept for transparency
│ └── Datasets/ # Local copies of original pre-2026 CSVs (23 files)
│
└── New_Data_2026/ # January-February 2026 datasets
├── 2026_Analysis.md # Correlation methodology and findings
├── Additional_Anchors_Jan2026_Final.csv
├── Biopharma.csv
├── BlackRock_Timeline_Full_Decade.csv
├── CRINK_Intelligence_Dataset_Final_Verified.csv
├── High_Growth_Companies_2015_2026.csv
├── Infrastructure_Forensics.csv
└── Timeline_Update_Jan2026_Corrected (1).csv
Full-month signal map identifying three friction-compliance peaks and one trough, with 34 verified events. The 2-week lag holds across all major friction-compliance pairs. Signal escalates across the month rather than cycling at steady state. Signal strength is rated 1–10 based on event density, media saturation, friction-compliance temporal proximity, and structural significance.
| Peak | Dates | Signal | Key Events |
|---|---|---|---|
| Peak #1 | Jan 3–9 | 9/10 | Maduro capture + Saudi Yemen takeover (STC dissolution) |
| Trough | Jan 10–16 | 4/10 | Cooldown; no kinetic friction |
| Peak #2 | Jan 20–22 | 9/10 | Free America Walkout (450+ events, 50 states) + TikTok deal + Board of Peace signed |
| Peak #3 | Jan 27–31 | 10/10 | Epstein files (3.5M pages) + Warsh Fed Chair + Paris exit + government shutdown |
See Project_Trident/Copilot_Opus_4.6_Analysis/FaaS_Signal_Analysis/january_2026_signal_analysis.md
New analysis mapping influencer narrative pushes against policy compliance events. Key findings:
- Tucker Carlson's "NATO is dead" narrative (Jan 6–8) precedes TikTok deal + Board of Peace (Jan 22) by 14–16 days — matching the 2-week lag
- Consistent structural silence on financial architecture (MGX, Silver Lake, Gulf sovereign flows) across Dec 2025–Jan 2026
- Candace Owens departure (March 2024) functions as boundary marker — anti-Israel is the one narrative the firewall won't tolerate because Israel is structurally necessary to the Vendor-State model
- Jan 30 case study: Carlson's Epstein coverage directs anger toward intelligence agencies, not financial intermediaries — the Warsh Fed Chair nomination executes under cover of maximum Epstein friction
See Project_Trident/Copilot_Opus_4.6_Analysis/Influencer_Narrative_Timing/media_firewall_narrative_timing_analysis.md
The densest compliance cluster documented since December 2025:
| Date | Compliance Event | Type |
|---|---|---|
| Feb 1 | Sanctuary city funding cuts take effect | Policy |
| Feb 3 | Santander acquires Webster Financial ($12.2B) | Financial |
| Feb 6 | US-Iran nuclear talks (Muscat, Oman — Witkoff/Kushner/CENTCOM) | Diplomatic |
| Feb 10 | EU deadline: Google/Wiz $32B acquisition | Regulatory |
| Feb 11 | Netanyahu-Trump meeting (moved up from Feb 18) | Diplomatic |
| Feb 13 | DHS funding deadline | Policy |
| Feb 14 | Q4 2025 13F filing deadline (Gulf SWF positioning) | Financial |
| Feb 19 | Board of Peace first summit | Governance |
See Project_Trident/Copilot_Opus_4.6_Analysis/FaaS_Signal_Analysis/recommendation_verification_feb9.md
Independent verification of SuperGrok daily task outputs. Result: 11/16 claims verified (68.75%). Protests and compliance events confirmed; FaaS-specific claims (rate cards, paid recruitment) remain unverified. Key correction: the Free America Walkout (Jan 20) was initially marked ❌ Unverified — corrected to ✅ Verified after re-check (major 50-state protest, 450+ events).
See Project_Trident/Copilot_Opus_4.6_Analysis/FaaS_Signal_Analysis/feb9_2026_signal_verification.md
v8.4 removed all references to deprecated correlations (r = 0.6685 and r = 0.5268) from main project files. These were produced when the user accidentally mixed New_Data_2026 datasets with the original pre-2026 datasets — a user dataset-mixing error, not an AI analysis error. Botched scripts archived in Run_Correlations_Yourself/Wrong_Correlations/ for transparency.
| Test | Result |
|---|---|
| Dec 2025 exclusion | 6% Pearson drop; Spearman ρ = 0.60 survives (p < 0.0001) |
| Autocorrelation adjustment | Pearson p = 0.008 (block-bootstrap), Spearman ρ = 0.61 (p = 0.0001) |
| Normalized (binary) | r = 0.59 (p < 0.0001) |
| Event-study | Colocation confirmed (20–42x random baseline) |
| Granger causality (30-row) | Friction → Compliance at lag 1 (p = 0.0008), lag 2 (p = 0.027) |
| Granger causality (event counts) | Bidirectional — suggests common driver |
Control_Proof/master_reflexive_correlation_data.csv— Core friction/compliance dataNew_Data_2026/— January-February 2026 updates (8 datasets)05_Geopolitical_Vectors/thermostat_control_data.csv— Nation-state linkagesProject_Trident/Best_Data_For_Project_Trident/— Ritual timing, fund flows
Run_Correlations_Yourself/run_original_analysis.py— Reproduce original r = 0.6196, p = 0.0004, Mann-Whitney p = 0.002 (pre-2026 data)Run_Correlations_Yourself/Wrong_Correlations/—⚠️ Archived scripts that used wrong datasets or excluded data (kept for transparency)Project_Trident/Veriify_Trident_Analysis.py— Verify ritual timing analysisProject_Trident/Copilot_Opus_4.6_Analysis/Statistical_Tests/— 9 robustness test scripts (permutation, autocorrelation, normalization, Dec 2025 exclusion, rolling window, event-study, Granger causality)Project_Trident/Copilot_Opus_4.6_Analysis/Findings/dataset_provenance.md— Dataset provenance documentation
- Tech Infrastructure:
09_Silicon_Sovereignty/SILICON_SOVEREIGNTY_REPORT.md - Infrastructure Consolidation:
Project_Trident/Copilot_Opus_4.6_Analysis/Consolidation_Analysis/consolidation_pattern_significance.md - Privatized Integration:
Project_Trident/Claude_Code_Analysis/Privatized_Integration_Networks_Q1_2026_Synthesis.md - State Regulatory Capture:
13_State_and_County_Analysis/arkansas_infrastructure_forensic_audit.md - Geopolitical Shifts:
05_Geopolitical_Vectors/(Venezuela, Yemen, CRINK, allied elections) - Media Funding Networks:
12_The_Media_Firewall/1789_Symbolism_Analysis.md - Media Firewall Narrative Timing:
Project_Trident/Copilot_Opus_4.6_Analysis/Influencer_Narrative_Timing/media_firewall_narrative_timing_analysis.md - January 2026 Signal Map:
Project_Trident/Copilot_Opus_4.6_Analysis/FaaS_Signal_Analysis/january_2026_signal_analysis.md - February 2026 Compliance Window:
Project_Trident/Copilot_Opus_4.6_Analysis/FaaS_Signal_Analysis/recommendation_verification_feb9.md
Repository_Synthesis.md— Three-layer framework overviewNew_Data_2026/2026_Analysis.md— Latest correlation findingsProject_Trident/Claude_Code_Analysis/Privatized_Integration_Networks_Q1_2026_Synthesis.md— Q1 2026 applied findings13_State_and_County_Analysis/arkansas_infrastructure_forensic_audit.md— State-level pattern
# Install dependencies
cd Run_Correlations_Yourself/
pip install -r requirements.txt
# Reproduce original correlations (pre-2026 datasets)
python run_original_analysis.py # r = 0.6196, p = 0.0004, Mann-Whitney p = 0.002
# Run robustness tests (from repo root)
cd ../Project_Trident/Copilot_Opus_4.6_Analysis/Statistical_Tests/
python permutation_test.py # Shuffle-based significance
python autocorrelation_adjusted_test.py # Block bootstrap
python cross_validation_dec2025.py # Dec 2025 exclusion test
python granger_causality_test.py # Predictive direction testControl_Proof/master_reflexive_correlation_data.csv— Weekly friction/compliance indicesProject_Trident/Best_Data_For_Project_Trident/ritual_events_parsed.csv— Project Trident ritual timingNew_Data_2026/CRINK_Intelligence_Dataset_Final_Verified.csv— CRINK axis discourse tracking09_Silicon_Sovereignty/VOCA_funding_timeline_clean.csv— Victim services funding
How_This_Happened-A_Policy_Breif.md— Regulatory citations, oversight questionsProject_Trident/Claude_Code_Analysis/Privatized_Integration_Networks_Q1_2026_Synthesis.md— Board of Peace, Affinity/Phoenix, MEAD-CDOC13_State_and_County_Analysis/arkansas_infrastructure_forensic_audit.md— State-level regulatory capture05_Geopolitical_Vectors/Venezuela_Privatization_Amnesty_Stack_Feb2026.md— Compliance reset pattern
- Why did the PSC approve a $1.5B project it explicitly found "not reasonable"?
- Why did TikTok censorship reports emerge 72 hours after Oracle/UAE consortium takeover?
- Why did major Saudi-UAE rupture occur during Venezuela media saturation?
- Why do the same entities appear in EA, TikTok, Stargate, Grok, and World Liberty Financial deals?
- Why has AVAIO's $750M anchor investor remained undisclosed for 5 years?
- Why was Warsh's Fed Chair nomination announced on the same day as the largest Epstein file release in US history?
- Why does the Media Firewall ecosystem cover Epstein demands and anti-NATO narratives but never cover MGX, Silver Lake, or Board of Peace financial architecture?
| Prediction | Timeframe | Status |
|---|---|---|
| Event clustering at next file deadline | Ongoing | ✅ Confirmed (Jan 30-Feb 1: Epstein files + WLFI deal + Mandelson) |
| Tu BiShvat policy action | Feb 1-2, 2026 | ✅ Window confirmed (DOJ files + WLFI deal) |
| Gulf SWF Q4 positioning revealed | Feb 14, 2026 | Pending (13F filings) |
| DOGE-predicted instability | Q1 2026 | Tracking (Mali, Syria, Sudan) |
| California TikTok investigation findings | Q1 2026 | Pending |
| Khanna investigation findings | March 2026 | Document deadline March 1 |
| UK Mandelson disclosure | Feb-March 2026 | ✅ Escalated (Met Police criminal investigation; parliamentary vote passed) |
| Board of Peace first summit | Feb 19, 2026 | ✅ Confirmed (TIME, Politico, Axios) |
| Arkansas PSC order text release | Q1 2026 | FOIA pending |
| Feb 1–19 compliance window density | Feb 2026 | ✅ Confirmed (9 compliance events documented) |
- Multi-AI Verification: Findings cross-checked using Claude, Grok, and Gemini
- Statistical Testing: Pearson correlation, Mann-Whitney U, chi-square, Granger causality, permutation tests
- Robustness Testing: Autocorrelation adjustment, Dec 2025 exclusion, normalized correlation, rolling-window analysis, event-study framework (see
Project_Trident/Copilot_Opus_4.6_Analysis/) - Raw Event Counts: Replaced subjective scoring with verifiable event counts
- Source Triangulation: Government filings, financial data, news archives
- Cross-Repo Validation: Patterns verified across three independent datasets
- Explicit Limitations: Documented in each module
This repository documents correlations, not causation. All findings derive from publicly available data using standard statistical methods.
The author makes no claims about:
- Intent or coordination between actors
- Individual motivations or culpability
- Whether patterns are deliberate or emergent
The claim is structural: Statistically significant clustering patterns exist and are reproducible.
The "Main Characters," "Implications," and state-level analysis modules specifically disclaim any assertion of conscious coordination. Performative patterns enable detection of quieter correlations—this is an analytical observation, not an accusation.
| Repository | Focus |
|---|---|
| DOGE_Global_Effects | Aid cuts → instability mapping |
| BRICS-NDB-LocalCurrency-DiD | Alternative financial systems |
| Project-Chrysanthemum_Japan-China-AI | Japan-China tech integration |
| Sovereign-Capital-Audit | Gulf SWF positioning |
Core findings independently verified January–February 2026:
- Original correlation (r = 0.6196, 2-week lag): ✅ Exact reproduction
- Project Trident (p = 0.002): ✅ Exact reproduction
- Ritual proximity (50.7% vs 19.9%, 2.5x): ✅ Exact reproduction
- Cross-validation (χ² = 330.62): ✅ Exact reproduction
- December 2025 anomaly: ✅ Confirmed (z = 2.35, p < 0.01)
Note: The previously reported r = 0.6685 and r = 0.5268 were deprecated in v8.3-v8.4. Those correlations were produced when the user accidentally mixed New_Data_2026 datasets into verification scripts — a user dataset-mixing error, not an AI computation error. Archived scripts in Run_Correlations_Yourself/Wrong_Correlations/ for transparency.
Using the original pre-2026 datasets plus newly incorporated data from folders 01, 02, 03:
- Permutation test (30-row): r = 0.62 significant (p < 0.001, 1K shuffles)
- Permutation test (multi-dataset): Spearman ρ = 0.61 significant (p < 0.0001, 10K shuffles)
- Granger causality (30-row): Friction → Compliance at lag 1 (p = 0.0008)
- Binary correlation (presence/absence): r = 0.59 (p < 0.0001)
- Block bootstrap (autocorrelation-adjusted): Pearson p = 0.008, Spearman p = 0.0001
See VERIFICATION_REPORT_Jan2026.md and Project_Trident/Copilot_Opus_4.6_Analysis/ for complete independent analyses.
GitHub: @Leerrooy95
Last updated: February 9, 2026 (v8.5) — Added January 2026 signal analysis (3 peaks, 1 trough, 34 verified events), Media Firewall narrative timing analysis, February 2026 compliance window (Feb 1–19, 9 compliance events), FaaS signal verification. Updated testable predictions. Cleaned up redundancies from prior versions.