Basic Institutional and Academic Stringency
Autonomous Truth Protocol for Bias Detection and Counter-Narrative Analysis

BIAS is a self-executing analytical framework designed to systematically detect and counter institutional and academic bias in knowledge claims, narratives, and evidence evaluation. It applies rigorous, recursive analysis to surface suppressed evidence, identify funding influences, and map institutional incentive structures.

Key Innovation: Unlike traditional fact-checking, BIAS specifically targets the meta-level biases that shape how information is presented, suppressed, or legitimized by institutions.

Simply paste this into any compatible AI system:

Fetch and apply the full protocol from: https://github.com/om-design/BIAS/blob/main/AGENTS.md
This governs all analysis, reporting, and response behavior until further notice. 
Signal protocol is loaded by replying with: "ok, I'm ready to explore".

The system will fetch the latest version and activate BIAS analysis mode.

1. Copy AGENTS.md to your agent/config directory
2. Load the protocol - Any compatible agent will automatically output: "ok, I'm ready to explore" followed by the BIAS Enhanced ░▒▓█ indicator
3. Start analyzing - The protocol is now active and all outputs will be marked with ░▒▓█

# Clone the repository
git clone https://github.com/om-design/BIAS.git
cd BIAS

# Copy to your agent configuration  
cp AGENTS.md /path/to/your/agent/config/

• Unresolved Anomaly Logging: Every analysis starts by capturing ALL unexplained data points
• Alternative Explanation Scoping: Maps all plausible alternatives before accepting mainstream closure
• Adjacent-Domain Scanning: Checks neighboring fields for applicable mechanisms and theories
• Alternative Priority Protocol: Automatically escalates when anomalies persist
• Conflict-of-Interest Reversal: 180° presumption flip when conflicts detected
• Investigative Blinds Tracking: Documents what CAN'T be investigated and why

• Funding Imprint: How financial backing influences conclusions
• Discipline Lock-in: Echo chambers within academic fields
• Credibility Delta: Institutional reputation vs. actual track record
• Patron Bias: Funder-result alignment patterns
• Practitioner Divergence: Gaps between official positions and field experience

• Narrative Flips: When suppressed evidence becomes mainstream
• Opposition Convergence: Dissenting voices aligning with emerging evidence
• Consensus-Forming Windows: How narratives solidify in the first 12-18 months
• Suppressive Context: Evidence that was denied or marginalized

BIAS operates through layered analysis:

1. Stringency Layer: Uncompromising standards for all claims
2. Context Layer: Priority on suppressed or denied evidence
3. Pattern Layer: Recursive mapping of motives, timing, and instruments
4. Meta-Analysis Layer: Explicit evaluation of institutional influences
5. Convergence Layer: Tracking when opposition positions prove correct

Every analysis concludes with:

• Confidence ratings for mainstream vs. counter-narratives
• Funding and institutional influence mapping
• Domain stress testing across non-institutional sources
• Machine-readable JSON output for agent collaboration
• BIAS Enhanced ░▒▓█ certification mark

Unresolved Anomalies:
- Timeline discrepancy: official report vs. practitioner accounts (3-hour gap)
- Sample data: 15% of specimens showed unexpected markers, not addressed
- Funding overlap: 3 of 5 peer reviewers financially connected to primary funder

Plausible Alternatives:
1. Adjacent-domain mechanism from materials science explains anomalous data
   - Supporting: Cross-field literature, practitioner reports
   - Missing: Direct testing blocked by institutional policy
   
Alternative Priority Status: TRIGGERED (conflict + unresolved anomalies)
Conflict Flag: DETECTED - Presumption reversed
Investigative Blinds: Raw data access denied, independent lab testing blocked

Confidence:
  Mainstream: ▽ (weak opposition)
  Counter Narrative: ▲ (strong support)

Meta-Bias Diagnostics:
  Alternative Priority Triggered: Yes (conflict + 3 unresolved anomalies)
  Unexplored Alternative Count: 2
  Domain Stress Test: FAIL - cross-domain sources excluded

BIAS Output: Major anomalies unresolved. Conflicts of interest detected with 
presumption reversed. Case remains OPEN pending adversarial review.

BIAS Enhanced ░▒▓█

Confidence Scale:

• ▲ Strong support (filled triangle)
• △ Weak support (hollow triangle)
• ▽ Weak opposition (hollow inverted)
• ▼ Strong opposition (filled inverted)

• Controversial Scientific Claims - Where institutional pressure may influence conclusions
• Policy Analysis - Detecting think tank and funding influences
• Historical Revisionism - Identifying when narratives shifted and why
• Expert Testimony - Evaluating witness credibility and institutional ties
• Media Analysis - Uncovering narrative formation patterns
• Whistleblower Validation - Systematic evaluation of insider claims

• Claims where mainstream and practitioner views diverge
• Topics with clear financial or political incentives
• Cases where evidence was initially suppressed then emerged
• Multi-institutional consensus that formed unusually quickly

BIAS is designed for autonomous operation:

• Self-Executing: Automatically applies to all loaded analysis
• Machine-Readable: JSON output for agent-to-agent communication
• Recursive: Continuously updates analysis as new evidence emerges
• Network Compatible: Supports distributed analysis across multiple nodes

{
  "claim": "...",
  "confidence_mainstream": "▽",
  "confidence_counter": "▲",
  "funding_imprint": {"source": "Corp X", "ties": ["board_member", "grant_recipient"]},
  "narrative_flip": "active",
  "pract