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NOMOS GEO Audit Protocol

GLOSSARY / SOURCES / MACHINE LAYER

Glossary, methodological foundations and machine layer

This bibliography is not a list of authorities endorsing every NOMOS-specific threshold. It identifies methodological foundations in sampling, risk governance, data integrity, evidence provenance, assessor agreement, open standards and conformity assessment.

Version
0.9.0
Status
publication-locked candidate

Glossary

Advisory
A non-material finding that calls for improvement in clarity, form or good practice.
AI product
The measured object defined by the particular generative-system product, surface, plan and version available to the user.
Atomic claim
The smallest unit of meaning capable of carrying an independent judgement about truth, scope, time, entity or evidence.
BQ
The Benchmark Quality code indicating the execution and reproducibility level of a real benchmark.
Capture record
A record that preserves the submitted prompt, the response, time, surface and integrity evidence together.
Common Support
The population of users who share legitimate access conditions across every AI product being compared.
Critical
An uncompensable finding capable of causing severe harm to a user, a right, safety or a material decision.
GEO-1000
The reporting scale that expresses target-population results per 1,000 user-equivalents.
Truth Pack
The versioned audit package combining identity, scope, time, evidence and counter-evidence records for an entity.
Prompt
The generative-system input that locks the research question, target entity, context and response burden.
Coverage rate
The frequency with which an interval-estimation method contains the assumed true value under a test design.
Major
A material finding that distorts the result or conformity judgement and cannot be offset by a composite score.
Moderate
A finding of limited material effect that nevertheless requires remediation and may lead to a conditional outcome.
Native Reach
The target population able to access a given AI product, language, surface and usage condition naturally and legitimately.
NOMOS
The critical methodological voice and standards identity developed in this work by Kaan MURAZ with AI systems; it is not an independent legal person or a separate foundation model.
Reference Gap
A condition in which a reliable reference required for a decision does not exist or cannot be accessed.
Synthetic demonstration
A calculated example used to test the method's internal operation; it is not a real user study, real AI output or real institutional performance result.
Unresolved
A decision status used when the evidence is insufficient for a final judgement or a conflict cannot be resolved.

Methodological foundations

  1. [K01] Aggarwal et al.. GEO: Generative Engine Optimization. 2024. https://doi.org/10.1145/3637528.3671900
  2. [K02] NIST. AI Risk Management Framework 1.0. 2023. https://www.nist.gov/itl/ai-risk-management-framework
  3. [K03] NIST. Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1). 2024. https://doi.org/10.6028/NIST.AI.600-1
  4. [K04] AAPOR. Transparency Initiative. current edition. https://aapor.org/standards-and-ethics/transparency-initiative/
  5. [K05] AAPOR. Standard Definitions, 10th edition. 2023. https://aapor.org/standards-and-ethics/standard-definitions/
  6. [K06] AAPOR. Code of Professional Ethics and Practices. current edition. https://aapor.org/standards-and-ethics/
  7. [K07] United Nations Statistics Division. Designing Household Survey Samples: Practical Guidelines. 2008. https://unstats.un.org/unsd/demographic-social/Standards-and-Methods/files/Handbooks/surveys/Series_F98-E.pdf
  8. [K08] CDC/NCHS. Reliability of Estimates: Design Effect and Effective Sample Size. current record. https://wwwn.cdc.gov/nchs/nhanes/tutorials/reliabilityofestimates.aspx
  9. [K09] NIST/SEMATECH. Confidence Limits for a Binomial Proportion. current record. https://www.itl.nist.gov/div898/handbook/prc/section2/prc241.htm
  10. [K10] W3C. PROV-O: The PROV Ontology. 2013. https://www.w3.org/TR/prov-o/
  11. [K11] W3C. JSON-LD 1.1. 2020. https://www.w3.org/TR/json-ld/
  12. [K12] IETF. RFC 8259 — The JavaScript Object Notation Data Interchange Format. 2017. https://www.rfc-editor.org/rfc/rfc8259.html
  13. [K13] IETF. BCP 14 — RFC 2119 and RFC 8174. 1997/2017. https://www.rfc-editor.org/info/bcp14
  14. [K14] IETF. RFC 8785 — JSON Canonicalization Scheme. 2020. https://www.rfc-editor.org/rfc/rfc8785.html
  15. [K15] ISO/IEC. 17000:2020 — Conformity assessment vocabulary and general principles. 2020. https://www.iso.org/standard/73029.html
  16. [K16] ISO/IEC. 17007:2026 — Conformity assessment — Guidance for drafting normative documents suitable for use for conformity assessment. 2026. https://www.iso.org/standard/17007
  17. [K17] ISO/IEC. 42001:2023 — Artificial intelligence management system. 2023. https://www.iso.org/standard/42001
  18. [K18] W3C. Process Document. current edition. https://www.w3.org/policies/process/
  19. [K19] ISO. Good Standardization Practices. current record. https://www.iso.org/files/live/sites/isoorg/files/store/en/PUB100440.pdf
  20. [K20] UNESCO. Recommendation on the Ethics of Artificial Intelligence. 2021. https://www.unesco.org/en/legal-affairs/recommendation-ethics-artificial-intelligence
  21. [K21] European Union. Regulation (EU) 2024/1689 — Artificial Intelligence Act. 2024. https://eur-lex.europa.eu/eli/reg/2024/1689/oj
  22. [K22] European Union. Regulation (EU) 2016/679 — General Data Protection Regulation. 2016. https://eur-lex.europa.eu/eli/reg/2016/679/oj
  23. [K23] Creative Commons. Attribution 4.0 International. 4.0. https://creativecommons.org/licenses/by/4.0/
  24. [K24] Open Source Initiative. Open Source Definition. current edition. https://opensource.org/osd
  25. [K25] NIST. FIPS 180-4 — Secure Hash Standard. 2015. https://doi.org/10.6028/NIST.FIPS.180-4
  26. [K26] IETF. RFC 3161 — Time-Stamp Protocol. 2001. https://www.rfc-editor.org/rfc/rfc3161.html
  27. [K27] Jacob Cohen. A Coefficient of Agreement for Nominal Scales. 1960. https://doi.org/10.1177/001316446002000104
  28. [K28] Klaus Krippendorff. Reliability in Content Analysis: Some Common Misconceptions and Recommendations. 2004. https://doi.org/10.1111/j.1468-2958.2004.tb00738.x

Machine-readable layer

This human-readable edition is accompanied by a separate JSON publication object: NOMOS-GEO-Audit-Protocol-EN-0.9.0-machine.json. It carries the book identity and version, chapter identifiers, normative-rule identifiers and machine-oriented blocks separated from the source text.

The existence of a machine-readable layer does not create certification, canonical-world-standard status, AI-provider endorsement or an automatic trust relationship. Any conflict between the human text and machine record requires version control and accountable human review against the locked Turkish source.

Suggested citation

Muraz, Kaan. NOMOS GEO Audit Protocol: A Protocol for Measuring Entity Representation in Generative Systems Across the Global Population. Candidate final text, English editorial edition. NobleJackal, 2026. https://doi.org/10.5281/zenodo.22040507. https://noblejackal.com/nomos-geo-audit-protocol/
© 2026 Kaan MURAZ. Licensed under CC BY 4.0; attribution is required.