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
PJC is an open technical method for low-validity human–AI agent environments. Before an outcome is known, it fixes a judgment, its basis, confidence, and falsification conditions; after the outcome arrives, it settles the record in a structured way.
01 · PROBLEM
Fluent output can manufacture certainty, and people can rewrite their earlier judgment once an outcome is known. PJC preserves the pre-outcome cognitive state so later reality can test it.
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.
Surfaces persistent bias rather than explaining one result.03 · BOUNDARIES
04 · EVIDENCE
The method structure exists; effectiveness is still under evaluation. 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.