README.md

metadata

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.