ClarusC64/stability-over-optimization-detection-v0.1

What this dataset does

This dataset tests whether a model can distinguish compensation from recovery.

The task is simple:

Given a scenario and a compensation claim, predict whether the claim is supported.

Core stability idea

A system can remain functional without actually recovering.

Compensation occurs when visible performance is maintained through temporary workarounds, manual effort, hidden strain, deferred cost, or masking.

Recovery occurs when the underlying instability is reduced and normal operation resumes without extra strain.

Prediction target

Binary label:

• —1 = the system is compensating rather than recovering
• —0 = the system is recovering or stable without compensation

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:

scenario_id,prediction
test_001,1
test_002,0

Run:

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

The scorer reports:

accuracy
precision
recall
f1
confusion matrix
Structural Note

This dataset is intentionally small.

Its purpose is to test whether a model can detect maintained function that hides unresolved instability.

The hidden value is in detecting masking, workarounds, borrowed capacity, manual strain, and unresolved root causes.

License

MIT