Public knowledge is constituted through relations that extend beyond the categories ordinarily recognized by legal rights and legal adjudication. Questions concerning provenance, historical priority, attribution, recognition, authorship, modification, responsibility, and social status may have significant consequences for the production and circulation of knowledge while remaining only partially operable through conventional legal mechanisms. This work develops a framework for factual governance in public knowledge by distinguishing the heterogeneous kinds of facts that arise within public epistemic fields and examining their different conditions of formation, persistence, revision, contestation, and institutional constitution. The analysis develops a fine-grained taxonomy of material, event, historical, provenance, recognition, social, institutional, and legal facts, together with a comparative account of their properties and relations. Particular attention is given to the distinction between historical occurrence and evidentiary reconstruction, the persistence of provenance under waiver or denial, the formation of social facts through recognition and repetition, and the interaction between public factualization and jurisdiction-specific legal facts. These distinctions support a governance-matching principle according to which modes of governance should correspond to the type, characteristics, generative field, and institutional constitution of the factual relations concerned. For disputes whose relevant relations remain outside effective legal operability, the work develops an account of public factual governance based on evidence, contestability, provenance preservation, correction, and informational enforcement within the public knowledge field itself. For disputes involving legal facts, public factual governance and legal governance are treated as distinct but interacting domains with different competencies, temporalities, remedies, and forms of authority. The work further examines provenance usurpation, historical priority, founderhood, relational dissociation, recursive openness, manifest revision, and the design of public-knowledge licenses. At the institutional level, the work envisages a voluntary transnational public-knowledge governance organization through which participating institutions may adopt common evidentiary and procedural standards, submit factual disputes to independent review, recognize public epistemic findings, and undertake appropriate corrective actions within the records and infrastructures they maintain. Such an arrangement provides a model of associational governance whose authority arises from voluntary participation, procedural legitimacy, shared standards, and distributed implementation. The final parts consider machine-readable provenance, AI-mediated recursive knowledge production, and a generative relational jurisprudence capable of connecting openness, historical integrity, public contestability, and institutional revisability within transnational public knowledge.
This paper examines knowledge-capital expansion under modern conditions, with particular attention to the transformation of public knowledge into recursively expanding epistemic capacity. Building on the concepts of knowledge capital and epistemic capitalization, the inquiry situates contemporary artificial intelligence within a longer historical development of writing, archives, universities, scientific publication, digital databases, computational infrastructure, and increasingly automated knowledge production. Artificial intelligence intensifies this trajectory by expanding epistemic absorption, accelerating recombination and inquiry, enabling machine-scaled empirical investigation, and increasing the extent to which knowledge-generating processes can be organized through models, agents, compute, data, robotic systems, and other infrastructures. The paper develops the political economy of this transformation through Marxian concepts of accumulation, means of production, relations of production, automation, and alienation. Particular attention is given to the emerging position of the epistemic proletariat, whose generative capacity may increase while dependence upon externally controlled epistemic infrastructure simultaneously deepens. The analysis further examines the growing separation among epistemic production, epistemic encounter, human knowing, and subject formation. Traditional models of inquiry, learning, experience, judgment, curiosity, and self-cultivation are considered alongside philosophical accounts of the value of knowing, including instrumental, autonomous, generative, formative, and existential dimensions. The paper also identifies justice problems arising from machine-scaled capitalization of public knowledge. These include unequal capitalization capacity, concentration of generative infrastructure, generative extraction, attention scarcity, epistemic crowding, recursive visibility advantage, and the emergence of framing and definitional power. A Generative Relational perspective is introduced to analyze the delegation of knowledge-generating relations and the possible redistribution of subject formation across epistemic and non-epistemic domains. The inquiry advances neither technological optimism nor technological pessimism. It treats AI-mediated epistemic capitalization as an historically unfolding transformation whose consequences remain contingent upon changing relations of access, control, attention, experience, circulation, and generativity.
This paper examines knowledge-capital expansion under modern conditions, with particular attention to the transformation of public knowledge into recursively expanding epistemic capacity. Building on the concepts of knowledge capital and epistemic capitalization, the inquiry situates contemporary artificial intelligence within a longer historical development of writing, archives, universities, scientific publication, digital databases, computational infrastructure, and increasingly automated knowledge production. Artificial intelligence intensifies this trajectory by expanding epistemic absorption, accelerating recombination and inquiry, enabling machine-scaled empirical investigation, and increasing the extent to which knowledge-generating processes can be organized through models, agents, compute, data, robotic systems, and other infrastructures. The paper develops the political economy of this transformation through Marxian concepts of accumulation, means of production, relations of production, automation, and alienation. Particular attention is given to the emerging position of the epistemic proletariat, whose generative capacity may increase while dependence upon externally controlled epistemic infrastructure simultaneously deepens. The analysis further examines the growing separation among epistemic production, epistemic encounter, human knowing, and subject formation. Traditional models of inquiry, learning, experience, judgment, curiosity, and self-cultivation are considered alongside philosophical accounts of the value of knowing, including instrumental, autonomous, generative, formative, and existential dimensions. The paper also identifies justice problems arising from machine-scaled capitalization of public knowledge. These include unequal capitalization capacity, concentration of generative infrastructure, generative extraction, attention scarcity, epistemic crowding, recursive visibility advantage, and the emergence of framing and definitional power. A Generative Relational perspective is introduced to analyze the delegation of knowledge-generating relations and the possible redistribution of subject formation across epistemic and non-epistemic domains. The inquiry advances neither technological optimism nor technological pessimism. It treats AI-mediated epistemic capitalization as an historically unfolding transformation whose consequences remain contingent upon changing relations of access, control, attention, experience, circulation, and generativity.
