phronesis · integrated coherence model
Logic Engine
Classification against rules you declare. It does not reason — it decides which of your categories a text falls into, and then accounts for every decision, including the ones it did not make.
That is the part worth having. “Why did you flag this” is a question anything can answer plausibly, including a model that is guessing. “Why did you not flag this” is what a review actually asks, and a model cannot answer it at all — there is no trace, and asking again may give a different answer. Click any rule below.
your rules · any / all / none, with weights
the text to classify
verdict
margin is a gap to the runner-up, not a probability. This engine has no basis for a confidence and will not invent one.
every rule considered — click one to ask why not
This is a lookup against a trace, not a generated explanation. The same rules and the same text give the same answer every time, and the reason is the actual cause rather than a plausible one.
What it is not: it has no semantics. It cannot tell “I love this” from “I do not love this” unless you write a none term for the negation. It is a better-behaved regex with an audit trail — which is a defensible thing to hand a regulated buyer, and a dishonest thing to call reasoning.