A.19.USCM:2 - Problem

Engineering teams often need to convert an admitted (indicator or NCV) profile into one or more score measures for downstream comparison and selection. If scoring is not given a first‑class mechanism boundary with explicit admissibility and evidence surfaces, the following failure modes are common:

• Illicit arithmetic by convenience: teams apply weighted sums, averages, or nonlinear transforms across mixed scale kinds without an explicit admissibility profile, creating scores that are not CSLC‑lawful.
• Hidden normalization: scoring implementations silently normalize, align, or flip polarities, collapsing the distinction between “normalize” and “score” and making downstream reasoning non‑reproducible.
• Silent scalarization: multi‑criteria realities (vector scores, partial‑order comparability) are reduced to a single scalar via hidden tie‑breakers, producing an apparent total order that is not justified.
• Unknown coercion: missing or insufficient evidence is coerced into 0/false or treated as “good enough,” yielding scores that look precise while being epistemically unsafe.
• Drift and non-auditability: different teams score the same admitted scoring target differently because admissibility constraints and effective policies (editions, evidence rules, crossings) are not explicit and not recorded.