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--- ---
schema: foundry-doc-v1 schema: foundry-doc-v1
title: "Ontological Governance" title: "Ontological Governance"
slug: ontological-governance slug: ontological-governance
category: governance category: governance
type: topic type: topic
quality: stub quality: stub
short_description: "Ontological governance describes the four self-healing control ledgers that govern how service-content classifies and accumulates knowledge, and the human-verification loop that keeps extracted identity data accurate before it is permanently committed." short_description: "Ontological governance describes the four self-healing control ledgers that govern how service-content classifies and accumulates knowledge, and the human-verification loop that keeps extracted identity data accurate before it is permanently committed."
status: active status: active
bcsc_class: public-disclosure-safe bcsc_class: public-disclosure-safe
last_edited: 2026-04-30 last_edited: 2026-04-30
editor: pointsav-engineering editor: pointsav-engineering
cites: [] cites: []
paired_with: ontological-governance.es.md paired_with: ontological-governance.es.md
--- ---
Automated classification systems drift over time — categories multiply, vocabulary fractures, and extracted data accumulates errors faster than they can be corrected. **Ontological governance** prevents this through two structural mechanisms: four throttled control ledgers that define classification vocabulary at intentionally slow update rates, and a human-verification loop that forces extracted identity fragments through human review before they are permanently written into the verified ledger. These two mechanisms are structurally separate but serve the same goal: preventing accumulated classification drift from undermining the integrity of long-lived institutional data. For a regulated operator, this means the platform's knowledge graph remains auditable and its identity records remain accurate without continuous manual curation. Automated classification systems drift over time — categories multiply, vocabulary fractures, and extracted data accumulates errors faster than they can be corrected. **Ontological governance** prevents this through two structural mechanisms: four throttled control ledgers that define classification vocabulary at intentionally slow update rates, and a human-verification loop that forces extracted identity fragments through human review before they are permanently written into the verified ledger. These two mechanisms are structurally separate but serve the same goal: preventing accumulated classification drift from undermining the integrity of long-lived institutional data. For a regulated operator, this means the platform's knowledge graph remains auditable and its identity records remain accurate without continuous manual curation.
## The three-stage extraction pipeline ## The three-stage extraction pipeline
Data extraction across the platform is mechanically isolated into Data extraction across the platform is mechanically isolated into
three services: three services:
1. **`service-email` (ingestion).** Processes MIME payloads and 1. **`service-email` (ingestion).** Processes MIME payloads and
deposits raw text and CSV files into the spool. No deposits raw text and CSV files into the spool. No
classification is applied at this stage. classification is applied at this stage.
2. **`service-people` (identity resolution).** Scans the spool 2. **`service-people` (identity resolution).** Scans the spool
for human identity clusters and routes them to the verification for human identity clusters and routes them to the verification
surveyor before committing to the verified ledger. surveyor before committing to the verified ledger.
3. **`service-content` (linguistic classification).** Scans the 3. **`service-content` (linguistic classification).** Scans the
spool for narrative knowledge and cross-references text against spool for narrative knowledge and cross-references text against
the four control ledgers. the four control ledgers.
## The four control ledgers ## The four control ledgers
`service-content` is governed by four CSV ledgers that update at `service-content` is governed by four CSV ledgers that update at
heavily throttled rates to preserve longitudinal data stability: heavily throttled rates to preserve longitudinal data stability:
| Ledger | Minimum update interval | Governs | | Ledger | Minimum update interval | Governs |
|---|---|---| |---|---|---|
| Archetypes | More than 24 months | The psychological and functional identity of the firm (for example, "The Fiduciary") | | Archetypes | More than 24 months | The psychological and functional identity of the firm (for example, "The Fiduciary") |
| Chart of Accounts | 18–24 months; requires executive override | The structural and financial geometry of the operation (for example, "Compliance", "IT Support") | | Chart of Accounts | 18–24 months; requires executive override | The structural and financial geometry of the operation (for example, "Compliance", "IT Support") |
| Domains | More than 12 months | Bilingual glossaries defining the macro-categories: Corporate (Finance), Projects (Real Estate), Documentation (Technology) | | Domains | More than 12 months | Bilingual glossaries defining the macro-categories: Corporate (Finance), Projects (Real Estate), Documentation (Technology) |
| Themes | 3–8 months | The active frontline narratives (for example, "Co-Location Expansion") | | Themes | 3–8 months | The active frontline narratives (for example, "Co-Location Expansion") |
Update rates are intentionally asymmetric. The slowest ledgers Update rates are intentionally asymmetric. The slowest ledgers
(Archetypes, Chart of Accounts) capture what the firm fundamentally (Archetypes, Chart of Accounts) capture what the firm fundamentally
is; the fastest (Themes) capture what it is currently working on. is; the fastest (Themes) capture what it is currently working on.
Premature updates to the slower ledgers corrupt the longitudinal Premature updates to the slower ledgers corrupt the longitudinal
coherence of the data corpus. coherence of the data corpus.
## The verification loop ## The verification loop
`service-people` uses a human-in-the-loop verification step to `service-people` uses a human-in-the-loop verification step to
prevent automated extraction errors from entering the verified prevent automated extraction errors from entering the verified
ledger. The process is described in detail at ledger. The process is described in detail at
[[verification-surveyor|Verification Surveyor]]. In brief: [[verification-surveyor|Verification Surveyor]]. In brief:
the system isolates unverified identity fragments for operator the system isolates unverified identity fragments for operator
review; the operator verifies each entity using their own personal review; the operator verifies each entity using their own personal
browser and off-network lookup; the verified result is then browser and off-network lookup; the verified result is then
committed to the ledger. The daily throughput limit ensures that committed to the ledger. The daily throughput limit ensures that
operator attention remains high-fidelity rather than habitual. operator attention remains high-fidelity rather than habitual.
## See Also ## See Also
- [[verification-surveyor|Verification Surveyor]] - [[verification-surveyor|Verification Surveyor]]
- [[message-courier|Message Courier Service]] - [[message-courier|Message Courier Service]]
- [[moonshot-initiatives|Moonshot Initiatives]] - [[moonshot-initiatives|Moonshot Initiatives]]
- [[sovereign-replacement-initiative|Sovereign Replacement Initiative]] - [[sovereign-replacement-initiative|Sovereign Replacement Initiative]]
## References ## References