Organizing Knowledge by Constraint and Complexity: A Four-Realm Taxonomy as Diagnostic Overlay for Mapping Human Agency Within Nested Cosmic, Planetary, Ecological, and Civilizational Systems.

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Title: Organizing Knowledge by Constraint and Complexity: A Four-Realm Taxonomy as Diagnostic Overlay for Mapping Human Agency Within Nested Cosmic, Planetary, Ecological, and Civilizational Systems.
Authors: Alwis, Harendra1 Harendra.Alwis@alumni.unimelb.edu.au
Source: Knowledge Organization. Jun2026, Vol. 53 Issue 3, p1-14. 14p.
Subjects: Taxonomy, Agent (Philosophy), Ecosystems, Astronomy, Systems theory, Metaphysical cosmology
Abstract: The horizontal fragmentation of human knowledge—its organization by disciplinary convention rather than by the structure of reality—is not merely an inconvenience for librarians but an active contributor to civilizational crisis. The idea that reality is structured into integrative levels has a long history in knowledge organization, from Comte's hierarchy of sciences through Hartmann's stratified ontology to Gnoli's Integrative Levels Classification. Yet no existing system fully develops the practical consequences of this ontological structure for delineating human agency, generating predictive capacity, or reconciling ideological conflict. This paper proposes a four-realm taxonomy—Cosmological, Planetary, Ecological, and Civilizational—that extends the integrative levels tradition by introducing three contributions: a Constraint-Agency Matrix formalizing the gradient of human agency across ontological levels, a thermodynamically bounded principle of downward causation, and a diagnostic-overlay that complements rather than replaces existing operational classification systems. Adopting a structural realist position, the taxonomy treats the constraint hierarchy as a mind-independent feature of reality while acknowledging that representations of it are fallible. It is designed to accommodate all materialist cosmologies and civilizational structures within a single architecture. The framework is governed by three meta-principles—Upward Constraint, Upward Emergence, and Downward Causation—and yields two distinctive capabilities that the existing literature has not fully developed: a differentiating power that organizes knowledge by ontological dependency rather than disciplinary convention, and a predictive power that, in the manner of Mendeleev's table, identifies gaps in knowledge that horizontal classification conceals. The taxonomy is testable, falsifiable, and applicable to curriculum design, knowledge-quality assurance, civilizational risk analysis, and the reconciliation of polarized ideological positions. [ABSTRACT FROM AUTHOR]
Copyright of Knowledge Organization is the property of IMR Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Organizing Knowledge by Constraint and Complexity: A Four-Realm Taxonomy as Diagnostic Overlay for Mapping Human Agency Within Nested Cosmic, Planetary, Ecological, and Civilizational Systems.
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  Data: <searchLink fieldCode="DE" term="%22Taxonomy%22">Taxonomy</searchLink><br /><searchLink fieldCode="DE" term="%22Agent+%28Philosophy%29%22">Agent (Philosophy)</searchLink><br /><searchLink fieldCode="DE" term="%22Ecosystems%22">Ecosystems</searchLink><br /><searchLink fieldCode="DE" term="%22Astronomy%22">Astronomy</searchLink><br /><searchLink fieldCode="DE" term="%22Systems+theory%22">Systems theory</searchLink><br /><searchLink fieldCode="DE" term="%22Metaphysical+cosmology%22">Metaphysical cosmology</searchLink>
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  Data: The horizontal fragmentation of human knowledge—its organization by disciplinary convention rather than by the structure of reality—is not merely an inconvenience for librarians but an active contributor to civilizational crisis. The idea that reality is structured into integrative levels has a long history in knowledge organization, from Comte's hierarchy of sciences through Hartmann's stratified ontology to Gnoli's Integrative Levels Classification. Yet no existing system fully develops the practical consequences of this ontological structure for delineating human agency, generating predictive capacity, or reconciling ideological conflict. This paper proposes a four-realm taxonomy—Cosmological, Planetary, Ecological, and Civilizational—that extends the integrative levels tradition by introducing three contributions: a Constraint-Agency Matrix formalizing the gradient of human agency across ontological levels, a thermodynamically bounded principle of downward causation, and a diagnostic-overlay that complements rather than replaces existing operational classification systems. Adopting a structural realist position, the taxonomy treats the constraint hierarchy as a mind-independent feature of reality while acknowledging that representations of it are fallible. It is designed to accommodate all materialist cosmologies and civilizational structures within a single architecture. The framework is governed by three meta-principles—Upward Constraint, Upward Emergence, and Downward Causation—and yields two distinctive capabilities that the existing literature has not fully developed: a differentiating power that organizes knowledge by ontological dependency rather than disciplinary convention, and a predictive power that, in the manner of Mendeleev's table, identifies gaps in knowledge that horizontal classification conceals. The taxonomy is testable, falsifiable, and applicable to curriculum design, knowledge-quality assurance, civilizational risk analysis, and the reconciliation of polarized ideological positions. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Knowledge Organization is the property of IMR Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jun2026
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