| --- |
| language: en |
| license: mit |
| task_categories: |
| - text-classification |
| tags: |
| - structural-bottleneck |
| - stability-geometry |
| - reasoning |
| - clarus |
| - sios |
| size_categories: |
| - n<1K |
| pretty_name: Structural Bottleneck Classification v0.1 |
| --- |
| |
| # What this dataset does |
|
|
| This dataset tests whether a model can detect structural bottlenecks. |
|
|
| The task is simple: |
|
|
| Given a scenario and a structural-bottleneck claim, predict whether the claim is supported. |
|
|
| # Core stability idea |
|
|
| A structural bottleneck is a constraint that limits system performance regardless of improvements elsewhere. |
|
|
| Typical bottlenecks include: |
|
|
| - single approval points |
| - single processing nodes |
| - unique dependencies |
| - centralized routing |
| - irreplaceable personnel |
| - constrained resources |
|
|
| Removing a bottleneck often increases system capacity more effectively than optimizing surrounding components. |
|
|
| # Prediction target |
|
|
| Binary label: |
|
|
| - 1 = a structural bottleneck is present |
| - 0 = a structural bottleneck 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 |
|
|
| ```bash |
| 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 limiting constraints embedded in system structure rather than transient operational issues. |
| |
| License |
| |
| MIT |
