A.19.USCM:5 - Archetypal Grounding — informative

A.19.USCM:5.1 - Tell

Think of USCM as admissibility‑gated scoring:

• Input: “an admitted profile of measures for this exact bearer, criteria, scope and window, comparison basis, evidence policy, and result use, plus the CN-Spec and CG-Spec editions that declare those bounds”
• Output: “a set of score measures that downstream steps may compare/select on”

The key didactic boundary is: USCM is allowed to transform measures only within the admissibility surface (SCP+CSLC), and it must not hide normalization, aggregation, or ordering.

A.19.USCM:5.2 - Show — U.System

A program manager evaluates competing rollout plans for a product launch.

• The admitted profile includes measures like {Cost, LeadTime, Reliability, RiskExposure, CarbonPerUnit}.
• The CG‑Spec’s SCP admits only scale‑lawful transforms (e.g., monotone transforms on ratio/interval measures, explicit unit alignment rules, and prohibited operations on ordinal measures).
• USCM runs Score(...) and outputs a score profile such as {UtilityScore, RiskScore} rather than forcing a single number.
• A plan lacks sufficient evidence for RiskExposure for the named planning bearer, selected claim slices, and qualification window; ScoreEligibility returns degrade, and the audit records the effective MinimalEvidence policy and the exact CN-Spec and CG-Spec editions.

Downstream steps can now compare and select with an explicit audit trail, instead of pretending that “the score was objective.”

A.19.USCM:5.3 - Show — U.Episteme

A research lead compares several model families for deployment across heterogeneous environments.

• Indicators include calibration and robustness metrics; scoring is done using a calibrated probabilistic score plus uncertainty‑aware score dimensions.
• A post‑2015 practice example is to keep monotonicity and interpretability constraints explicit (e.g., monotone additive models or monotone deep lattice style models) and to treat uncertainty as first‑class (e.g., conformal set‑valued scoring that yields intervals rather than point scores).
• USCM produces a score profile that can remain vector‑valued and uncertainty‑aware, and it refuses to coerce “unknown” into a point score. Comparisons and selections occur downstream using set‑valued semantics where appropriate.