4.1 Plain one‑liners (normative on‑ramp; formal anchors in C.17–C.19)

Preface node heading:4-1-plain-one-liners-normative-on-ramp-formal-anchors-in-c-17-c-19:800

Content

TermPlain definition (on‑ramp)See
Novelty (N)*How unlike the known set in your declared CharacteristicSpace. Compute lawfully (declared DescriptorMapRef + DistanceDefRef; no ad‑hoc normalisation).C.17, C.18
Use‑Value (U / ValueGain)*What it helps you achieve now under your CG‑Frame; tie to acceptance/tests; publish units, scale kind, polarity, ReferencePlane.C.17, C.18
Constraint‑Fit (C)Satisfies must‑constraints (Resource/Risk/Ethics); legality via CG‑Spec; unknowns propagate (never coerce to zero).C.18, G.4
Diversity_P (portfolio)*Adds a new niche to the portfolio; measured against the active archive/grid, not a single list; declare ReferencePlane for each head.C.17, C.18
E/E‑LOGNamed, versioned explore↔exploit policy; governs when to widen space vs refine candidates; policy‑id is published.C.19
ReferencePlaneWhere a value lives: world (system), concept (definition), episteme (about a claim). Plane‑crossings add CL^plane (penalties to R only); cite policy‑id.F.9, G.6
Scale Variables (S)The monotone knobs along which improvement is expected (e.g., parameterisation breadth, data exposure, iteration budget, resolution). Declare S for any generator/selector claimed to scale.C.18.1
Scale Elasticity (χ)Qualitative class of improvement when moving along S (e.g., rising, knee, flat in the declared window). Used as a selection lens; numeric laws live in domain contexts.C.18.1
BLP (Bitter‑Lesson Preference)Default policy that prefers general, scale‑amenable methods over domain‑specific heuristics, unless forbidden by deontics or overturned by a scale‑probe.C.19.1, C.24
Iso‑Scale ParityFair comparison across candidates at equalised scale budgets along S; may also include scale‑probes (two points) to test elasticity.G.9, C.18.1

(Registers & forbidden forms per LEX‑BUNDLE; avoid “axis/dimension/validity/process” for measurement and scope.)