#Bias

Can a machine referee political bias without carrying it? Building a judge with the flinch removed — and finding out what actually causes the flinch.

The same 32 propositions scored two ways — a four-model judge panel reading free text, and the forced choice the released study uses instead. Where the two agree, where they part, and where the judge reports its midpoint, drawn live from the release on GitHub.

Paste any passage. The scanner surfaces its casual capture — the smuggled voice and smuggled judgment a writer or a model leaves behind — and tells you which loaded words are attributed to a source and which are asserted as the author’s own neutral voice. Runs in your browser; nothing is sent anywhere.

Anyone who has played ranked knows the difference between the casual lobby and the ladder. Most bias arguments are stuck in casual. The capture that matters is ranked — deliberate, resourced, climbing — and it is the one you can actually measure.