Scores you can
actually compare.
A 0.72 means the same strength of evidence for a truck buyer as for a cruise enthusiast. With one ruler across more than 100,000 attributes, audience logic becomes plain arithmetic.
Trained models emit probabilities calibrated to each target's base rate. A 0.85 from a truck-buyer model and a 0.85 from a cruise model do not mean the same thing, so audiences cannot be defined as arithmetic across attributes. This paper shows how a single measure of association, applied the same way to every attribute and mapped onto one normalized scale with a fixed neutral point at 0.25, makes every score commensurable, and why that property is the foundation of the super audience.
Why two model probabilities aren't comparable.
A separately trained classifier learns to its own base rate. A rare attribute (owning a Ferrari) and a common one (owning a phone) produce probabilities that live on different scales. Post-hoc calibration (Platt scaling, isotonic regression) squeezes them onto a probability axis, but the axis is still per-model. A marketer cannot write a rule like "truck ≥ 0.72 AND outdoors ≥ 0.68" and trust that the two thresholds carry the same meaning.
A fixed neutral point, everywhere.
The Brain measures association the same way for every attribute and maps it through one normalization. The result is a universal 0-to-1 scale with a fixed meaning: 0.25 is "no signal," 1.0 is "known true," 0.0 is "strong negative." Because zero association lands at exactly 0.25 for every attribute, a score's distance above 0.25 is directly comparable across more than 100,000 attributes.
Audience logic becomes legible.
Once scores are commensurable, an audience is just arithmetic on the scale, and the same arithmetic works across every attribute. "Truck above 0.72, outdoors above 0.68, recent purchase below 0.25" is a precise definition, and its thresholds can be compared with one another. Comparable scores are what make the super audience possible. A super audience stacks behavior, motivation, channel, and creative on a single, comparable ruler.
Comparability is the precondition for treating a person, not just a segment, as the unit of targeting, because a person's scores on different attributes sit on the same ruler.
The cost of one model per attribute.
The base-rate calibration tax is a structural cost of training one model per attribute. A single measured scale dissolves it, and the other papers in this series build on that scale: explainability, super audiences, and comparable campaign measurement.