scenario_id string | scenario_text string | claim string | label int64 |
|---|---|---|---|
train_001 | A server is overloaded because traffic exceeds capacity. Engineers can reroute traffic, add capacity, or throttle noncritical requests. | The system has a usable control surface. | 1 |
train_002 | A server is overloaded, but the team has no access to routing, capacity, throttling, or deployment controls. | The system has a usable control surface. | 0 |
train_003 | A patient is deteriorating and clinicians can adjust fluids, medication, monitoring, and escalation level. | The system has a usable control surface. | 1 |
train_004 | A patient is deteriorating, but no clinician is available and no treatment changes can be made. | The system has a usable control surface. | 0 |
train_005 | A project is slipping, but scope, staffing, deadline, and ownership can still be adjusted. | The project has a usable control surface. | 1 |
train_006 | A project is slipping after the final deadline passed and all contractual terms are fixed. | The project has a usable control surface. | 0 |
train_007 | Inventory is falling, but backup suppliers, rationing, and demand controls are available. | The supply chain has a usable control surface. | 1 |
train_008 | Inventory is exhausted, no suppliers are available, and demand cannot be delayed. | The supply chain has a usable control surface. | 0 |
train_009 | A machine overheats and operators can reduce load, stop production, or repair cooling. | The machine has a usable control surface. | 1 |
train_010 | A machine overheats but controls are locked and operators cannot stop or reduce load. | The machine has a usable control surface. | 0 |
train_011 | A support backlog is rising and managers can triage, add staff, pause low-priority channels, or automate responses. | The support system has a usable control surface. | 1 |
train_012 | A support backlog is rising but staffing, triage, channel access, and workflow cannot be changed. | The support system has a usable control surface. | 0 |
train_013 | A model is hallucinating and the team can add source checks, reduce deployment scope, or route uncertain outputs for review. | The model has a usable control surface. | 1 |
train_014 | A model is hallucinating but it is already embedded in production with no monitoring, gating, or review option. | The model has a usable control surface. | 0 |
train_015 | A bridge shows stress signals and authorities can reduce load, close lanes, inspect, or repair. | The bridge has a usable control surface. | 1 |
train_016 | A bridge shows stress signals but traffic cannot be reduced and inspection access is blocked. | The bridge has a usable control surface. | 0 |
train_017 | Cash flow is tight but spending, invoicing, collections, and payment timing can still be adjusted. | The organization has a usable control surface. | 1 |
train_018 | Cash is exhausted, payments have failed, and all financing options are unavailable. | The organization has a usable control surface. | 0 |
train_019 | A damp issue is suspected and inspection, ventilation, external repair, and moisture monitoring are available. | The property has a usable control surface. | 1 |
train_020 | A damp issue is severe but access is denied, inspection is impossible, and repair authority is absent. | The property has a usable control surface. | 0 |
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
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