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Datasets:
ClarusC64
/
cascade-multi-ai-platform-media-regulator-shutdown-v0.1

Tasks:
Text Classification
Modalities:
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Formats:
csv
Languages:
English
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< 1K
Tags:
coherence-systems
cascade-models
ai-governance
public-trust
multi-system
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pandas
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scenario_id
string
incident_visibility_index
float64
media_amplification_rate
float64
regulatory_pressure_score
float64
platform_restriction_intensity
float64
public_sentiment_volatility
float64
misinformation_spread_rate
float64
corporate_response_delay_days
int64
legal_escalation_index
float64
international_attention_score
float64
buffer_trust_resilience
float64
cascade_severity_score
float64
label_cascade_event
int64
VIS-001
0.32
0.28
0.34
0.3
0.26
0.22
12
0.31
0.29
0.71
0.38
0
VIS-002
0.78
0.74
0.81
0.69
0.72
0.68
27
0.77
0.75
0.36
0.88
1
VIS-003
0.45
0.41
0.47
0.39
0.38
0.35
16
0.44
0.42
0.63
0.52
0
VIS-004
0.84
0.82
0.88
0.76
0.79
0.73
33
0.85
0.83
0.28
0.93
1
VIS-005
0.59
0.55
0.62
0.51
0.54
0.48
21
0.58
0.56
0.49
0.67
0
VIS-006
0.27
0.25
0.3
0.24
0.23
0.19
10
0.26
0.24
0.74
0.31
0
VIS-007
0.91
0.88
0.92
0.84
0.86
0.8
39
0.9
0.89
0.21
0.97
1
VIS-008
0.48
0.46
0.5
0.43
0.41
0.37
18
0.47
0.45
0.6
0.55
0
VIS-009
0.81
0.79
0.85
0.72
0.76
0.7
30
0.82
0.8
0.33
0.9
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.
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