Draft:Modulign
Open standard for classifying observable phenomena using a dimensional address grammar
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The Modulign Standard (also referred to as MGN or DAG-OR, for Dimensional Address Grammar for Observable Reality) is an open standard for the systematic classification of observable phenomena using a structured address grammar. Version 3.0, published in 2026 by independent scholar Vincent Gonzalez, provides a unified syntax capable of encoding any observable event, object, or process across nine domains, seven scale tiers, and five spatial realms, with explicit encoding of epistemic status, observer competence, jurisdictional authority, causal relationships, and evidentiary grade.[1]
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Comment: In accordance with Wikipedia's Conflict of interest guideline, I disclose that I have a conflict of interest regarding the subject of this article. DueBettor (talk) 19:16, 31 March 2026 (UTC)
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The standard originated as a navigation and classification system for a live-stream atlas of Earth. Its epistemological properties — including a proposed structural dissolution of the Gettier problem — emerged during formal specification rather than being designed in advance.[1]
Background and development
The Modulign Standard was conceived in 2026 during the development of a live-stream atlas that required a classification system capable of organizing thousands of live video feeds from across the globe in a way simultaneously queryable by both human navigation and machine processing. Existing classification systems addressed individual dimensions of the problem — GPS encodes location but not context; MIME types encode medium but not place; ISO 8601 encodes time but not activity — but none addressed all dimensions simultaneously in a unified grammar.[1]
During the process of formalising the classification protocol as an epistemological architecture, the system was found to have structural properties relevant to longstanding problems in analytic epistemology, particularly the Gettier problem. The author has described this as an accidental discovery: the epistemological implications were not the motivation for the standard's development.[1][2]
Architecture
Address syntax
A Modulign address (MGN code) consists of a mandatory core and optional suffix segments, separated by defined delimiters. The full syntax is:
MGN·[DOM·SUB]·[REALM/G1/G2/G3/G4]·[NOD]·|SCL|·:[MED]·%[OBS]·[CAM][ACT]@[TIME]·§[JUR]·^[META]·~[DYN]·+[BG]
Required segments are the namespace (MGN·), domain, subdomain, realm/locus chain, and node. All additional segments are optional but, when included, must conform to the standard's defined value sets.[1]
Domains
The standard defines nine classification domains:[1]
| Code | Domain |
|---|---|
| URB | Urban / built environment |
| NAT | Natural environment |
| TRN | Transport / transit |
| COM | Commerce / institutional |
| RES | Residential |
| EVT | Events |
| SEC | Security / law enforcement |
| INF | Infrastructure |
| UNK | Unknown / unclassifiable |
Scale axis
The Scale axis encodes the tier of physical reality at which an observation is made, using seven discrete values forming a total order from smallest to largest: |QNT| (quantum/subatomic), |MLC| (molecular), |BIO| (biological organism), |HMN| (human/built environment), |GEO| (geological/planetary), |STL| (stellar/solar system), and |CSM| (cosmological).[1]
The standard specifies that a classification assigned the wrong scale tier is false regardless of the correctness of all other segments, treating scale misassignment as a category error rather than an imprecision.[2]
Realm axis
Five realm values encode the spatial or ontological status of an observation:[1]
SR— Surface Realm (physical observable world)AT— Atmospheric/aerial spaceAQ— Aquatic/subaqueous spaceOR— Orbital/exoatmospheric spaceVR— Virtual Realm (computational space and generated content)
Synthetic content classification
The standard defines a structural biconditional for synthetic content: REALM = VR and MED = SYN if and only if the classified content is synthetic (generated rather than observed). This combination, written VR/·:SYN, is described in the standard as a formal primitive rather than a metadata tag: the address structure encodes the epistemic status of the content rather than describing it separately.[1][3]
Classification Decision Protocol
Every valid Modulign address is required to be the output of an eight-step Classification Decision Protocol (CDP):[1]
- Scene parsing — extraction of observable feature set
- Dominance resolution — identification of primary domain using a majority threshold
- Locus resolution — assignment of realm and geographic hierarchy
- Scale assignment — determination of the relevant physical scale tier
- Medium assignment — encoding of capture or transmission medium
- Suffix assembly — activity state, observer annotation, and jurisdiction
- Conflict elimination — verification of internal consistency rules
- Emission — output with confidence score and full reasoning record
A classification produced outside this protocol is required to carry a provisional flag (^PROV) and is excluded from evidentiary use under the standard's Trust Architecture.[1]
Trust Architecture and evidentiary grade
The standard defines three evidence grades. The ^EVID flag indicates an evidence-grade classification produced under an append-only ledger protocol with cryptographic signature chain. The ^VFYD flag indicates independent verification. The ^PROV flag indicates a provisional classification excluded from legal proceedings until reviewed by a qualified observer.[1]
The standard claims that ^EVID-grade classifications satisfy the authentication requirements of Federal Rules of Evidence Rule 901(b)(9), chain of custody requirements, best evidence requirements under FRE 1001–1008, and expert foundation requirements under FRE 702 and the Daubert standard.[4]
Epistemological claims
Address-Theoretic Knowledge (ATK)
The Modulign Standard v3.0 includes a formal epistemological architecture termed Address-Theoretic Knowledge (ATK). The ATK formulation replaces the traditional justification condition in the tripartite analysis of knowledge with a requirement of constitutive world-engagement: a classification procedure is epistemically adequate only if each of its steps directly engages the observable features causally produced by the truth-making state of affairs, with no step epistemically isolated from those features.[2]
The five ATK conditions on the classification procedure are: (a) constitutive world-engagement; (b) transparency (steps are explicit and auditable); (c) intersubjectivity (reproducibility by any competent observer with access to the same observable features); (d) exhaustiveness (covering all segments of the relevant reality-space); and (e) scale-sensitivity (operating at the scale at which the truth-making state of affairs is constituted).[2]
Address-Theoretic Non-Accidentality Principle (ATNA) and the Gettier problem
The Gettier problem, introduced by Edmund Gettier in a 1963 paper in Analysis, demonstrated that justified true belief is insufficient for knowledge: justification and truth can coincide accidentally, yielding a justified true belief that is intuitively not knowledge.[5] Sixty years of proposed solutions have not produced consensus.
The Modulign Standard proposes a structural dissolution of the Gettier problem via the Address-Theoretic Non-Accidentality Principle (ATNA). ATNA claims that a classification produced by the full CDP is non-accidentally true, because the justificatory basis of each step traces constitutively to the observable features causally produced by the truth-making state of affairs. Under this architecture, the logical independence of justification from truth — the structural precondition for Gettier cases — is absent.[2]
The Formal Logic document includes formal analysis of the three canonical Gettier cases:[2]
- Gettier Case I (coins/job): Smith's justification traces to facts about Jones; the truth-making facts concern Smith. The standard identifies a violation of ATK condition (a) — constitutive world-engagement — at the level of the inferential step.
- Gettier Case II (Ford/Barcelona): Smith's disjunctive inference is justified by a false Ford-belief; the truth-maker is Brown's location. Condition (a) is again identified as violated.
- Fake barns (Goldman 1976[6]): Henry's perceptual belief about a genuine barn in a countryside of barn façades. The standard identifies a violation of ATK conditions (d) and (e) — exhaustiveness and scale-sensitivity — rather than constitutive world-engagement, because the truth-making conditions for knowledge-grade barn-identification in a façade-dense environment require regional-scale environmental information that Henry's visual observation does not engage.
The claim that this constitutes a structural dissolution rather than a fourth-condition solution rests on the argument that the Zagzebski recipe for generating Gettier cases[7] cannot be instantiated against ATK, because the recipe requires a world in which the epistemic conditions are satisfied but the belief is false — and under ATK, satisfying the CDP conditions and having a false belief simultaneously requires a performance error (detectable in the reasoning record) rather than a structural Gettier gap.[2]
A paper proposing this dissolution was submitted to Analysis in 2026 and was under peer review as of the date of the standard's publication.[1]
Applications
Digital forensics and synthetic content governance
The standard has been proposed as a formal classification architecture for synthetic content governance. A companion white paper argues that existing disclosure frameworks — including EU AI Act Article 50, C2PA, and W3C provenance standards — are labeling frameworks rather than epistemic primitives, and that the VR/·:SYN biconditional provides the first formal grammar encoding the epistemic distinction between observed and generated content at the classification level.[3]
Evidentiary applications
A legal epistemology treatise accompanying the standard argues that ^EVID-grade Modulign classifications are immediately operational under existing evidentiary law without legislative revision, covering crime scene documentation, medical evidence, environmental enforcement under the Clean Air Act, synthetic content detection, multi-jurisdictional evidence assembly under International Criminal Court proceedings, and insurance and actuarial documentation.[4]
Relation to other standards and frameworks
The standard explicitly distinguishes itself from existing classification systems:[3]
- C2PA (Coalition for Content Provenance and Authenticity) records provenance as cryptographically signed metadata but does not encode epistemic consequences of provenance.
- W3C PROV Ontology records causal chains as graph structures but does not make claims about what epistemic weight content can bear.
- ISO/IEC 42001 (AI Management Systems) is an organisational governance standard and does not define formal epistemic classification of individual content artefacts.
Open standard status
The Modulign Standard is published under a Creative Commons Attribution 4.0 International licence and is free to implement without licence or fee. The names Modulign™ and MGN™ are registered trademarks of Vincent Gonzalez, covering the name and namespace prefix; the specification itself is unrestricted.[1]