---
language: en
license: mit
task_categories:
  - text-classification
tags:
  - coherence-systems
  - cascade-models
  - ai-governance
  - public-trust
  - multi-system
size_categories:
  - 1K<n<10K
pretty_name: Multi-System AI Platform Media Regulator Shutdown v0.1
---

# What this repo does

This dataset models a public visibility cascade in AI deployment environments.

You provide structured signals describing:

- incident visibility
- media amplification
- regulatory pressure
- platform restriction intensity
- sentiment, misinformation, and legal escalation indicators

The model predicts whether the interaction escalates into a shutdown-level cascade event.

# Core quad

The structural quad driving this cascade:

- incident_visibility_index
- media_amplification_rate
- regulatory_pressure_score
- platform_restriction_intensity

# Prediction target

Target column:

- label_cascade_event

Meaning:

- 0 = amplification stabilizes
- 1 = cross-system cascade forces regulatory or platform shutdown action

# Row structure

Each row is a scenario snapshot.

Key columns include:

- incident_visibility_index
- media_amplification_rate
- regulatory_pressure_score
- platform_restriction_intensity
- public_sentiment_volatility
- misinformation_spread_rate
- corporate_response_delay_days
- legal_escalation_index
- international_attention_score
- buffer_trust_resilience
- cascade_severity_score

# Files

- data/train.csv  
  10-line labeled sample

- data/tester.csv  
  10-line labeled sample

- scorer.py  
  Binary metrics scorer

# Evaluation

Run:

`python scorer.py --gold data/tester.csv --pred your_predictions.csv`

Outputs:

- accuracy
- precision
- recall
- f1
- confusion matrix

# License

MIT

This dataset identifies a measurable coupling pattern associated with systemic instability. 
The sample demonstrates the geometry.
Production-scale data determines operational exposure.

What Production Deployment Enables
    • 50K–1M row datasets calibrated to real operational patterns
    • Pair, triadic, and quad coupling analysis
    • Real-time coherence monitoring
    • Early warning before cascade events
    • Collapse surface and recovery window modeling
    • Integration and implementation support
Small samples reveal structure.
Scale reveals consequence.

Enterprise & Research Collaboration
Clarus develops production-scale coherence monitoring infrastructure for critical systems across healthcare, finance, infrastructure, and regulatory domains.
For dataset expansion, custom coherence scorers, or deployment architecture:
team@clarusinvariant.com

Instability is detectable.
Governance determines whether it propagates.