Method statement

Method

Rules the ranking cannot break

  1. Deterministic scoring. Identical inputs and weights produce identical ranks; reproducible from the audit package with one command.
  2. Provenance on every number: source, retrieved_at, confidence, licence note. A number without provenance does not exist.
  3. No silent imputation. Missing evidence is recorded as missing and penalized by a published rule, never estimated.
  4. Domains never cross-rank. Books, papers, reports, and standards are scored within their own domain.
  5. Each language ecosystem scores within itself first. Coverage gaps are declared, not hidden.
  6. People are context, not contestants. Persons, organizations, and platforms carry no score, ever.
  7. Manual decisions are records. Every override carries a written rationale and is published; Apparens-authored works are flagged.
  8. Humility on rank. A rank is a transparent output of declared evidence, weights, and missing-data rules at a release date, not a verdict on intrinsic worth.

Ontology v0.2 (frozen)

Canonical entities (book, paper, report, standard) are scored within their domain. Context entities (person, organization, platform) are described, never ranked: structurally, they carry no score field. Governance records (releases, challenges, overrides) are append-only.

Weighting scenarios

Scenariocitation_countlibrary_holdingsreadership_persistencesyllabus_adoptions
academic0.50.20.050.25
broad_influence0.20.250.40.15
governance_practitioner0.250.30.10.35

Missing-data penalty factor: 0.5. Normalization: per_domain_min_max. method_version 0.1-pilot. These are pilot placeholder weights; every change ships with a changelog entry.

What each signal means

Declared deferred capabilities

The method names these now and does not pretend they are done. Each is deferred openly, not silently stubbed:

Cite this method

The method is documented in a citable note (Corpus Cognitivum), archived with a DOI: doi.org/10.5281/zenodo.21042034 (concept DOI, always the latest version). It is licensed CC BY 4.0.

Janssen, J. (2026). The AI Canon: a method for auditable knowledge curation (Corpus Cognitivum). Apparens. https://doi.org/10.5281/zenodo.21042034