Datasetpublic

ClarusC64/stability-fragility-exposure-v0.1

What this dataset does This dataset tests whether a model can detect fragility exposure. The task is simple: Given a scenario and a fragility-exposure claim, predict whether the claim is supported. Core stability idea Fragility exposure measures how much damage can result from a small disturbance. Fragile systems show: single points of failure low redundancy narrow margins disproportionate consequences poor fault isolation Robust systems absorb local failures… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/stability-fragility-exposure-v0.1.

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Dataset Card

What this dataset does

This dataset tests whether a model can detect fragility exposure.

The task is simple:

Given a scenario and a fragility-exposure claim, predict whether the claim is supported.

Core stability idea

Fragility exposure measures how much damage can result from a small disturbance.

Fragile systems show:

—single points of failure
—low redundancy
—narrow margins
—disproportionate consequences
—poor fault isolation

Robust systems absorb local failures without widespread disruption.

Prediction target

Binary label:

—1 = fragility exposure is present
—0 = fragility exposure is not present

Row structure

Each row contains:

—scenario_id
—scenario_text
—claim
—label

Files

—data/train.csv
—data/test.csv
—scorer.py
—README.md

Evaluation

Create a predictions CSV with:

csv

scenario_id,prediction
test_001,1
test_002,0

Run:

python scorer.py --predictions predictions.csv --truth data/test.csv
Structural Note

This dataset is intentionally small.

Its purpose is to test whether a model can identify systems where small perturbations create disproportionately large consequences.

The hidden value is in detecting single-point dependency, missing redundancy, poor isolation, and amplified vulnerability.

License

MIT