ClarusC64/cascade-multi-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… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/cascade-multi-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:
- incidentvisibilityindex
- mediaamplificationrate
- regulatorypressurescore
- platformrestrictionintensity
Prediction target
Target column:
- labelcascadeevent
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:
- incidentvisibilityindex
- mediaamplificationrate
- regulatorypressurescore
- platformrestrictionintensity
- publicsentimentvolatility
- misinformationspreadrate
- corporateresponsedelay_days
- legalescalationindex
- internationalattentionscore
- buffertrustresilience
- cascadeseverityscore
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.