Epistemology of interpretive measurement
Interpretive measurement is not a promise of absolute truth. It is a disciplined way to compare outputs, qualify distortion, and make governance observable under declared conditions.
A metric becomes legitimate only when it remains attached to a canon, a perimeter, a response regime, and an evidence chain.
What measurement can claim
- that a given output stayed closer or farther from the canon under declared conditions;
- that some types of distortion recur more often than others;
- that stability or instability can be observed across prompts, models, or time windows.
What measurement cannot claim
- that the system has reached truth in the abstract;
- that a score alone proves compliance or authority;
- that a metric can replace an audit or a canonical reading.
Why the epistemology matters
Without an explicit epistemology, measurement is easily over-read. Scores become verdicts, observability becomes certification, and a descriptive signal is mistaken for an authorization to act.
Practical consequence
Interpretive measurement is therefore not an optimization race. It is a governance discipline. It exists to reduce category error, bound confidence, and support legitimate response rather than to maximize a dashboard number.