ClarusC64/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