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Paul S. Pruiett's Stratified Tri-level Holarchy: Difference between revisions
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[[ | ;a project by [[User:XenoEngineer|The Practitioner Emeritus]] | ||
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==== Pruiett’s Formalization Beyond Zenkin ==== | |||
According to direct practitioner experience, much of the SLIP and Knowledge Operating System architecture was developed in close dialogue with Paul Stephen Pruiett by many phone calls and working sessions. In this practice-based history, Pruiett is understood to have formalized a range of structures and processes (including stratified category-theoretic framing and knowledge-operating abstractions) that Zenkin had not explicitly formalized. | |||
On this account, Zenkin’s work on Social Control and value-synchronic patterns provides important intuitions, while Pruiett’s later framework supplies the missing formal machinery: stratified category theory, visualAbstractions as a 0 for KM, and the functorial 4-point / forest-of-pointers perspective that grounds SLIP’s live Markovian clustering over sparse logs. | |||
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Latest revision as of 02:09, 7 March 2026
- Tri-level Holarchy
- Prueitt, Paul S.
- Paul S. Pruiett's Stratified Tri-level Holarchy
- The Social Brain: Stratification Theory as Applied to Neural Architecture
- a project by The Practitioner Emeritus
Pruiett’s Formalization Beyond Zenkin
According to direct practitioner experience, much of the SLIP and Knowledge Operating System architecture was developed in close dialogue with Paul Stephen Pruiett by many phone calls and working sessions. In this practice-based history, Pruiett is understood to have formalized a range of structures and processes (including stratified category-theoretic framing and knowledge-operating abstractions) that Zenkin had not explicitly formalized.
On this account, Zenkin’s work on Social Control and value-synchronic patterns provides important intuitions, while Pruiett’s later framework supplies the missing formal machinery: stratified category theory, visualAbstractions as a 0 for KM, and the functorial 4-point / forest-of-pointers perspective that grounds SLIP’s live Markovian clustering over sparse logs.