Contribution-Weighted Multilayer Epistemic Graphs and Derived Multidomain Reputation A Conceptual and Operational Framework for Collective Knowledge Systems

<jats:p>&lt;p&gt;&lt;span&gt;Collective knowledge systems typically observe final products—articles, reports, decisions, and learning outcomes—more effectively than the processes through which those products are formed. Consequently, a substantial share of epistemic work remains poorly visible: conceptual clarification, error detection, reasoned objections, negative results, cross-domain connections, the articulation of uncertainty, and corrections prompted by subsequent outcomes. When evaluation resolution is low, participants must rely on aggregate indicators—status, affiliation, credentials, citation counts, or an author’s general reputation—that can become cognitive substitutes for directly assessing the epistemic value of a specific contribution.&lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;span&gt;This article proposes a contribution-first architecture in which the primary unit is the semantic block / atomic epistemic contribution: an appropriately bounded, semantically coherent, and traceable segment of textual interaction that performs a distinct epistemic function and can be evaluated in at least one relevant domain. Semantic blocks undergo independent domain-weighted team evaluation, reasoned processing of negative evaluations, successive external validation tiers, and updating through outcome feedback—evidential feedback derived from subsequent observable outcomes. The resulting contribution-weighted multilayer epistemic graph integrates the Knowledge, Knowledge Gaps, and Tension Layers and preserves accepted claims, structured ignorance, minority alternatives, conflicts, validation routes, and histories of change.&lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;span&gt;Within this framework, multidomain reputation is not a primary rating of a person. It is treated as a derived, probabilistic, contextual, and evolving second-order variable formed from the history of evaluated contributions, evaluation acts, argumentation, independent validation, and outcome feedback. This reputational model may exert a limited influence on the weight of future evaluation acts through domain-specific evaluator reliability, but it neither determines the truth of new contributions nor transfers automatically across domains. The framework also includes AI-assisted segmentation, recommendations of candidate domains, and choice among a bounded set of sufficiently differentiated alternatives, while final decisions remain human.&lt;/span&gt;&lt;/p&gt; &lt;p&gt;&lt;span&gt;The article specifies an evaluation lifecycle, principles of multi-tier validation, requirements for evaluator reliability, outcome feedback, and epistemic-state transitions, as well as governance constraints against status lock-in, social scoring, micro-surveillance, and enforcement overload. AI-assisted structuring and adaptive UI/UX with a dynamic governance layer are presented as dependent extensions of the core architecture rather than as completed modules equivalent to the central architectural contribution. The work is positioned as a conceptual and operational framework and a design science research agenda, not as a finalized mathematical or empirically validated system.&lt;/span&gt;&lt;/p&gt; &lt;div&gt; &lt;br&gt; &lt;/div&gt; &lt;div&gt; Multilayer Epistemic Graphs and Derived Multidomain Reputation | Preprint Version 1.0 &lt;/div&gt;</jats:p>