arXiv subjects

Denise M. Case

Publications and source records attributed to Denise M. Case.

3 recordsLinked to original sources

arXiv2026

Operational Identity: A Finite Audit of Declared and Implemented Rules of Sameness

A record system declares when two records refer to the same entity, occurrence, scope, or rule. Its disclosed implementation mechanisms induce a corresponding operational identity relation. The declared and implemented relations may diverge systematically without producing a provenance gap or detectable contradiction. A system can apply, consistently and with every record individually correct, a rule of sameness that no artifact declares. This paper formalizes that implemented relation. A declared identity regime partitions a finite record domain into co-reference classes; a disclosed mechanism, through its typed identity-relevant outcomes, induces an operational identity partition of the same domain. The audit compares these partitions in the refinement lattice. A mechanism is faithful when the declared partition refines the operational partition, so no declared class is split. A divergence witness is a pair the declaration merges and the mechanism separates; such witnesses are decidable by pair enumeration. When an imported sibling basis also splits a declared class, local comparison with its partition yields sibling-aligned, sub-sibling, super-sibling, or sibling-incomparable divergence. This result reports only the relationship; it does not identify the basis carried by the mechanism. Global equality of the operational and sibling partitions is defined separately as regime substitution and does not follow from sibling alignment. A version field incremented on every textual edit inhabits the sub-sibling case by splitting declared classes more finely than either imported basis. The audit is three-valued and relative to the disclosed artifacts, evaluated surfaces, and identified uses; each boundary has a finite refuting witness. A passing verdict is non-monotone because extending the transformation history can merge declared classes and create a witness among records already examined.

cs.LO↗

arXiv2026

Referential Regimes: Transformation-Invariant Identity for Neutral Substrates

Data systems increasingly operate under persistent legal, political, and analytic disagreement, where no single interpretive authority can be assumed. A neutral substrate provides stable shared reference without requiring agreement about causal or normative interpretation: it fixes reference structurally and leaves interpretation to extension layers. This paper derives the identity structure such a substrate requires using a small transformation algebra. Identity is treated as transformation-invariance: a referent is individuated by the admissible operations that preserve it. Once routine record operations are fixed, individuation becomes a structural calculation. That calculation exposes a mismatch between reference kinds and identity regimes. An accountability substrate must refer to six carrier kinds: obligation-bearers, rules, occurrences, scopes, records, and plain referents. Three kinds have more than one admissible basis. A scope may be fixed by extension or structure; a rule by content or structure; a plain referent by locus or object. Decomposition separates the scope readings, refinement separates the rule readings, and forking separates the plain-referent readings. Counting reference kinds gives six, while transformation analysis forces at least nine identity regimes. The three additional regimes add no new kind to the inventory. They arise from identity behavior under ordinary operations, and any attempt to collapse them reappears as a hidden regime that persistent disagreement cannot hold fixed. The result is a conditional lower bound. A richer transformation basis may leave the bound unchanged or force further splits, but it cannot erase a distinction witnessed here. The additional regimes correspond to distinctions often handled by modeling convention, but such conventions cannot substitute for regime-level identity commitments under persistent disagreement.

cs.LO↗

arXiv2026

Neutral Substrates: A Design Constraint for Shared Records Under Persistent Interpretive Disagreement

Shared accountability records are often used by parties who may never agree about causation, responsibility, or normative interpretation. For such records, neutrality cannot be achieved by omitting contested information, because accountability requires preserving the claims parties made, with their sources and provenance. Nor can neutrality be achieved by asserting one contested interpretation as the shared base. This paper defines a neutral substrate as a shared representational layer that provides stable reference while making no object-level substrate-layer commitments to causal or normative propositions. The central design constraint is that, when causal and normative propositions are contestable across admissible frameworks and the substrate's referential commitments are common ground, the substrate's neutrality is guaranteed at design time if and only if its foundational layer is restricted to those referential commitments and attribution propositions whose attributional basis is fixed by them. Causal and normative content may still be represented, but not as object-level foundational-layer commitments: it may appear there only as the content of attributed assertions with provenance, made by some identified framework, source, agent, institution, record, or document. The representational machinery used here is standard: reification, attribution, and provenance. The contribution is the constraint: a checkable condition on the foundational layer of a shared record, stated together with the assumptions it depends on and the boundary condition under which the constraint does not apply. A neutral substrate says enough to preserve accountability, but it does not turn one party's interpretation into an object-level substrate-layer commitment. The constraint does not apply at that layer when the referential regime or attributional basis is contested among the frameworks in play.

cs.AI↗