stg-scoring-problems

Score and rank customer problems by evaluating each candidate across frequency, severity, breadth, and alternatives' inadequacy with tiered evidence. This structured problem-scoring method produces a ranked set of candidate problems ready for hypothesis construction.

The four-property framework for problem-hypothesis construction scores candidate problems using frequency, severity, breadth, and alternatives' inadequacy. It outputs a registered hypothesis with claims, evidence, kill conditions, and elimination logs.

Build a ranked problem hypothesis by enumerating 5-7 candidate problems from governor input and public signals, then applying compression-model elimination to score them. The output includes claims, tiered evidence, and kill conditions in a register-ready format.

Yes, compression-model elimination filters inadequate problem candidates during the scoring process. It eliminates weaker candidates by compressing evidence against the four properties, producing a refined set of ranked problems.

Structured problem scoring uses governor input alongside public signals to enumerate 5-7 candidate problems. This grounds the initial problem set in validated context before applying the four-property evidence-based scoring framework.

Problem-scoring with a compression model is limited by the quality of initial governor input and public signals. If candidate problems lack sufficient evidence for frequency, severity, breadth, or alternatives' inadequacy, the elimination logs and resulting hypothesis may be incomplete.