| --- |
| language: en |
| license: mit |
| task_categories: |
| - text-classification |
| tags: |
| - stability-geometry |
| - compensation-vs-recovery |
| - reasoning |
| - clarus |
| - sios |
| size_categories: |
| - n<1K |
| pretty_name: Stability Compensation vs Recovery 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: |
|
|
| ```csv |
| 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 |
