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Ontological governance

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a07bdf56 · PointSav Digital Systems ·

docs(governance): D6 — governance category completion

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@@ -4,11 +4,11 @@ title: "Ontological governance"
slug: ontological-governance
category: governance
type: topic
quality: stub
quality: complete
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
bcsc_class: public-disclosure-safe
last_edited: 2026-04-30
last_edited: 2026-05-19
editor: pointsav-engineering
cites: []
paired_with: ontological-governance.es.md
@@ -52,15 +52,13 @@ coherence of the data corpus.

## The verification loop

`service-people` uses a human-in-the-loop verification step to
prevent automated extraction errors from entering the verified
ledger. The process is described in detail at
[[verification-surveyor|Verification Surveyor]]. In brief:
the system isolates unverified identity fragments for operator
review; the operator verifies each entity using their own personal
browser and off-network lookup; the verified result is then
committed to the ledger. The daily throughput limit ensures that
operator attention remains high-fidelity rather than habitual.
`service-people` uses a human-in-the-loop verification step to prevent automated extraction errors from entering the verified ledger. The process is described in detail at [[verification-surveyor|Verification Surveyor]]. In brief: the system isolates unverified identity fragments for operator review; the operator verifies each entity using their own personal browser and off-network lookup; the verified result is then committed to the ledger. The daily throughput limit ensures that operator attention remains high-fidelity rather than habitual.

## Why asymmetric update rates matter for regulated operators

The asymmetric ledger structure produces a property that matters in regulated contexts: the base of the knowledge graph is stable enough to audit. A procurement evaluator or compliance reviewer reading data extracted two years ago and data extracted last week will find them classified against the same Archetypes and Chart of Accounts taxonomy — the categories have not drifted. Only the Themes layer, which updates most frequently, reflects the current operational focus.

For financial disclosure purposes, this means that the platform's knowledge graph does not introduce spurious variation into the record. Consistent classification over time is not a side effect of discipline; it is a structural property enforced by the update-rate ledger. An auditor querying "what has this firm classified as Compliance over the past three years" receives a meaningful answer because the category boundaries have not shifted underneath the data.

## See also
Important Information

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