A.18:2 - Problem

Uninterpretable values. A raw number or label means nothing without knowing what aspect it measures and how it is measured. The string “4”, the label “High”, or the real number 9.81 convey no insight unless we know which Characteristic they pertain to and the Scale that gives them meaning. In cross-disciplinary work this ambiguity is magnified: a “5” could be a risk rank (ordinal), a length in meters (ratio), or a satisfaction score (perhaps interval). Common failure modes include:

• In ordinal settings (e.g. expertise levels Novice < Skilled < Expert), one can rank values but not meaningfully add or average them. Treating ordinal labels like numbers (e.g. averaging Novice=1, Expert=3) produces invalid results.

• In cardinal settings (e.g. seconds, meters, kelvins), arithmetic operations do make sense – but only if units are respected and zero is meaningful (for ratio scales). If we strip away units or mix scales (seconds vs. minutes), we again get nonsense.

Without a strict Standard, one team might treat “High” and “Medium” as having a numeric gap, another might average 4 (on a 5-star scale) with 4 (as 4 seconds) because both are “4”. Inconsistent practices make cross-domain reasoning impossible. We need a kernel-level solution that fixes: (a) the aspect being measured, (b) the scheme by which it’s measured, and (c) the type of scale structure (ordinal vs. metric), and that ensures each reported value is bound to that scheme. At the same time, the Standard should not force artificial numeric detail where it isn’t applicable (e.g. we shouldn’t assign meaningless numbers to purely qualitative tiers just to satisfy a structure).