Semantic Structure Learning: An Observer-Relative Framework for Claim-Bounded Research Under Uncertainty
Year: 2026
Type: preprint
Abstract: Research failures frequently arise not because a domain method is incapable, but because the method is applied to the wrong unresolved role, an observer cannot distinguish alternatives that matter to qualification, a provisional result is promoted beyond its quantifier, or an equivalent reformulation is mistaken for reduction of the remaining problem. These errors recur across otherwise unrelated domains, including mathematics, experimental science, software validation, artificial-intelligence evaluation, and complex-system design. This paper introduces Semantic Structure Learning (SeSL), a research-governance and relational-analysis framework intended to preserve these distinctions explicitly. SeSL separates role requirements from candidate solutions, observer sufficiency from evidence availability, source custody from truth, method applicability from residual reduction, and local results from the parent claim they are intended to support. Its stable form uses four object kinds—Designators, Semantic Crystals, Relational Crystals, and Bindings/Witnesses—across eight analytical levels: SEE, WORK, STRUCTURE, MEDIATE, OBSERVE, EVOLVE, MEASURE, and CONTRACT. The framework adds an unknown-first loop, observer requirement inversion, claim-relative source sufficiency, method quarantine, quantifier custody, exceptional-set sensitivity, residual-reduction auditing, explicit endpoint rebinding, and witnessed research handoff. The stable reference implementation passes 61/61 operator tests and 17/17 conformance checks; the release package passes 68/68 validation checks. These checks establish implementation and specification consistency, not independent scientific validation. SeSL does not replace mathematics, experiment, causal inference, programming, or domain expertise. Its narrower claim is that a reusable relational structure can preserve the objective, expose missing distinctions, qualify methods and evidence, and determine when a research process is—and is not—entitled to advance a claim.
Author: Jentges, Cecil
Language: English
Subfield: Management of Technology and Innovation
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7225457914