Prospective recording
Fix the judgment, basis, confidence, observation window, and falsification conditions before the outcome is known.
Produces an append-only commitment record.AN OPEN SEDES MENTIS TECHNICAL METHOD · RESEARCH PREVIEW v0.6
PJC FRAMEWORK
Prospective Judgment Calibration Framework
Before the outcome is known, write down what you expect, why you expect it, and what would show you were wrong. When the outcome arrives, review it against the original criteria.
PJC is an open technical method for low-validity human–AI agent environments: tasks where feedback is delayed and the relationship between cues and outcomes is weak or unstable. It organises this process through prospective recording, outcome settlement, and temporal calibration.
01 · PROBLEM
Fluent output can manufacture certainty, and people can rewrite their earlier judgment once an outcome is known. PJC preserves the judgment and supporting reasons written before the outcome so later reality can test them.
02 · METHOD
Fix the judgment, basis, confidence, observation window, and falsification conditions before the outcome is known.
Produces an append-only commitment record.After the observation window, settle against a pre-specified external outcome source.
Distinguishes supported, contradicted, inconclusive, and invalid records.Compare confidence, judgment types, and outcomes across repeated cycles.
Provides material for examining recurring judgment biases.03 · BOUNDARIES
04 · EVIDENCE
The method structure exists, and internal operability is under evaluation; calibration effects and business benefits have yet to be validated. PJC currently makes no promise of higher efficiency, accuracy, or team performance.
05 · DOCUMENTATION
Run a complete PJC cycle.
02Fields, states, and a worked example.
03Sources, mechanisms, and testable hypotheses.
04Structural constraints for agent systems.
05Current status and pilot standards.
06Evidence level and falsification criteria.
07Version and terminology history.
08CC BY 4.0 open license.