Ontological governance
editorial(governance): fix ontological-governance's fabricated throttling mechanism + fictional extraction pipeline (Track-B) — confirmed the 4 CSV ledgers (archetypes/chart_of_accounts/domains/themes) are real, served by service-content over a config HTTP endpoint, but zero code enforces the claimed 3-8/12/18-24/24-month update intervals anywhere; confirmed no update_interval/throttle logic exists in taxonomy.rs; confirmed the claimed 3-stage service-email-to-service-people-to-service-content spool pipeline has zero cross-references between those crates anywhere; reframed as reference vocabulary an operator manually consults (per the already-established Verification Surveyor finding), not an automated classifier; register-clean EN+ES
@@ -7,65 +7,43 @@ type: topic content_type: topic quality: complete index_group: platform-disciplines short_description: "The four self-healing control ledgers governing how service-content classifies and accumulates knowledge, and the human-verification loop that keeps it accurate." short_description: "Four reference vocabulary ledgers kept deliberately narrow, plus a human-verification loop that reviews extracted identity fragments before they enter the verified ledger." status: active bcsc_class: public-disclosure-safe last_edited: 2026-05-19 last_edited: 2026-08-22 editor: pointsav-engineering cites: [] 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** is this platform's answer: a small set of reference vocabulary ledgers, kept deliberately narrow, plus a human-verification loop that forces extracted identity fragments through human review before they are permanently written into the verified ledger. For a regulated operator, this means the platform's identity records stay accurate without continuous automated re-classification. ## The three-stage extraction pipeline ## The four vocabulary ledgers Data extraction across the platform is mechanically isolated into three services: [[service-content]] serves four CSV reference ledgers over a config HTTP endpoint (`/v1/config/*`): 1. **[[service-email]] (ingestion).** Processes MIME payloads and deposits raw text and CSV files into the spool. No classification is applied at this stage. 2. **[[service-people]] (identity resolution).** Scans the spool for human identity clusters and routes them to the verification surveyor before committing to the verified ledger. 3. **[[service-content]] (linguistic classification).** Scans the spool for narrative knowledge and cross-references text against the four control ledgers. | Ledger | Governs | |---|---| | [[archetypes-and-chart-of-accounts|Archetypes]] | Named functional roles a firm can occupy (for example, "The Fiduciary", "The Guardian") | | [[archetypes-and-chart-of-accounts|Chart of Accounts]] | The firm's personnel-role taxonomy (for example, "Compliance", "IT Support") | | Domains | Bilingual glossaries defining the platform's three subject-matter macro-categories: Corporate (Finance), Projects (Real Estate), Documentation (Technology) | | Themes | Named active initiatives (for example, "Co-Location Mandate Expansion") | ## The four control ledgers `service-content` is governed by four CSV ledgers that update at heavily throttled rates to preserve longitudinal data stability: | Ledger | Minimum update interval | Governs | |---|---|---| | [[archetypes-and-chart-of-accounts|Archetypes]] | More than 24 months | The psychological and functional identity of the firm (for example, "The Fiduciary") | | [[archetypes-and-chart-of-accounts|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) | | Themes | 3–8 months | The active frontline narratives (for example, "Co-Location Expansion") | Update rates are intentionally asymmetric. The slowest ledgers (Archetypes, Chart of Accounts) capture what the firm fundamentally is; the fastest (Themes) capture what it is currently working on. Premature updates to the slower ledgers corrupt the longitudinal coherence of the data corpus. These are reference vocabulary, not an automated classifier — no code cross-references incoming content against the ledgers' keywords to assign a category. The one confirmed consumer is [[verification-surveyor|Verification Surveyor]], where an operator manually tags an entity against the Archetypes/Chart of Accounts vocabulary during human review. Keeping the vocabulary small and infrequently revised is an editorial practice, not a rate-limited or code-enforced property of the ledgers themselves. ## 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 ## Why a stable vocabulary matters 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. A small, infrequently-revised vocabulary produces a property that matters in regulated contexts: the base of the knowledge graph stays legible to audit. A procurement evaluator or compliance reviewer reading data classified against the Archetypes and Chart of Accounts vocabulary a year apart finds the same category names in use — the taxonomy has not fragmented underneath the data. 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. For financial disclosure purposes, this means that the platform's identity records do not introduce spurious variation into the record. An auditor querying "what has this firm classified as Compliance over the past three years" receives a meaningful answer because the category names have not shifted underneath the data. ## See also - [[verification-surveyor|Verification Surveyor]] — the human-in-the-loop agent that verifies identity fragments before they enter the ledger - [[archetypes-and-chart-of-accounts]] — the two slowest-updating control ledgers that define firm identity and financial geometry - [[message-courier|Message Courier Service]] — the routing substrate that delivers inbound communications to service-email - [[moonshot-initiatives|Moonshot Initiatives]] — engineering program to replace quarantined dependencies that ontological governance depends on - [[worm-ledger-design]] — the append-only storage substrate that makes the verified ledger authoritative - [[verification-surveyor|Verification Surveyor]] — the human-in-the-loop agent that verifies identity fragments before they enter the ledger, and the one confirmed consumer of the Archetypes/Chart of Accounts vocabulary - [[archetypes-and-chart-of-accounts]] — the two ledgers that define firm identity and personnel-role taxonomy - [[worm-ledger-design]] — the append-only storage that makes the verified ledger authoritative