metadata
language: en
license: mit
task_categories:
- text-classification
tags:
- control-geometry
- control-surface
- reasoning
- clarus
- sios
size_categories:
- n<1K
pretty_name: Control Surface Classification v0.1
What this dataset does
This dataset tests whether a model can detect whether a system has usable control options.
The task is simple:
Given a scenario and a control-surface claim, predict whether the claim is supported.
Core stability idea
A control surface is the set of levers that can still change system trajectory.
A system has a usable control surface when actors can adjust pressure, buffer, timing, routing, access, scope, escalation, or recovery action.
A system lacks a usable control surface when meaningful levers are absent, blocked, too late, or outside available authority.
Prediction target
Binary label:
- 1 = the system has a usable control surface
- 0 = the system does not have a usable control surface
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 distinguish real agency from false agency.
The hidden value is in detecting available levers, blocked levers, authority limits, timing constraints, and remaining trajectory control.
License
MIT