Research and standards source ledger
Ledger version: 0.1.0
Evidence cutoff: September 1, 2026
Status: design input; no conformance, endorsement, validation, or legal-compliance claim
This ledger records the external sources that materially influenced the v0.1 public-draft foundation. It separates inspiration from adoption, and adoption from verified conformance. A citation here means “reviewed and relevant,” not “implemented,” “endorsed,” or “licensed for reproduction.”
Ledger rules
- Prefer official standards bodies, regulators, primary research, and first-party product methodology.
- Record the version or dated state reviewed; living sources are rechecked before any release.
- Summarize concepts rather than copying protected taxonomies or visual trade dress.
- Treat licenses, trademarks, and permissions as independent review items.
- Record departures as explicitly as alignments.
- Legal sources are monitoring inputs only. Applicability must be determined by qualified counsel for the product, jurisdiction, purpose, and customer deployment.
- A future conformance claim requires a separate clause-level mapping, implementation evidence, tests, review authority, and permission to make the claim.
Method, measurement, and publication sources
| ID | Source and state reviewed | Authority / status | Material influence on this draft | Explicit boundary or non-adoption | Review trigger |
|---|---|---|---|---|---|
| M-01 | Artificial Analysis benchmarking methodology, accessed 2026-08-31 | First-party operating benchmark methodology | Dense methodology navigation; explicit definitions; versioned indices; multiple dimensions; real-world context; chart-level provenance; live results separated from launch articles | No visual/trade-dress copying; no model-to-human construct analogy; no single global human index; no cost-per-person; no named-person leaderboard | Quarterly and before major public-site redesign |
| M-02 | Artificial Analysis Intelligence methodology, v4.1.1 page accessed 2026-08-31 | First-party current methodology | Publish task counts, repeats, conditions, scoring method, component weights, uncertainty, and limitations next to results | Its model-index weights and confidence claims do not transfer to people; LockedIn weights remain unvalidated hypotheses | Each cited method-version change |
| M-03 | Artificial Analysis articles, accessed 2026-08-31 | First-party editorial archive | Separate durable methodology, current explorer, dated launch analysis, and correction/version history | Frequency and visual format are inspiration, not evidence of LockedIn research maturity | Monthly product review |
| M-04 | NIST AI Risk Management Framework, AI RMF 1.0 plus 2026 revision notice | U.S. government voluntary framework; under revision | Govern–Map–Measure–Manage lifecycle; context, limits, human oversight, monitoring, contestability, and risk documentation | No NIST conformance or endorsement claim; watch the announced revision | On NIST revision and at least quarterly |
| M-05 | NIST AI RMF Playbook, living resource accessed 2026-08-31 | U.S. government implementation suggestions | Operational control prompts, affected-party participation, TEVV, documentation, fairness, privacy, and decommissioning | Not treated as a checklist or certification scheme | On Playbook revision |
| M-06 | NIST TEVV-Athlon initial public draft, NIST AI 200-2 IPD announced 2026-08-07 | U.S. government initial public draft; comment period open at cutoff | Objective-first and context-specific evaluation; events, tools, and measurement blocks; real-world impact and outcome framing | Draft status must remain visible; designed for AI systems, so human-assessment adaptation requires independent construct evidence | After comment-period outcome and final revision |
| M-07 | NIST AI Technology Evaluation, accessed 2026-08-31 | U.S. government evaluation program | Sequestered blind-data boundary, common plans/protocols, contamination control, and no-endorsement posture | No claim that LockedIn has equivalent infrastructure or NIST participation | Each AITE program revision |
| M-08 | MLCommons AILuminate safety methodology, accessed 2026-08-31 | Industry consortium benchmark method | Public practice versus hidden official material; explicit evaluator uncertainty and limitations; iterative benchmark program | Hazard taxonomies, grades, and official-test authority are not imported | At benchmark major releases |
| M-09 | METR RE-Bench repository and paper, reviewed 2026-08-31 | Primary benchmark implementation and research paper | Long, open-ended environments; human baselines; protected solution material; attempt-level conditions and citation-ready release artifacts | AI-agent-versus-human comparison does not validate FDE capability measurement; task duration is not a personal readiness score | Annual literature review and major release |
| M-10 | ISO/IEC 42001:2023, accessed 2026-08-31 | Published international standard; copyrighted | AI management-system lifecycle, assigned accountability, objectives, risk treatment, monitoring, and improvement as governance input | No certification or conformance claim; do not reproduce protected content without license review | On revision and before certification claims |
| M-11 | ISO/IEC 23894:2023, accessed 2026-08-31 | Published international standard; copyrighted | AI risk-management vocabulary and lifecycle input | No clause-level mapping or conformance claim | On revision |
| M-12 | ISO/IEC 17024:2026, edition 3 published 2026-03 | Published international personnel-certification standard; copyrighted | Impartiality, separation from training, consistent/reliable decisions, records, confidentiality, security, and human oversight of AI-assisted certification | v0.1 is not a certification scheme or accredited body; official certification is a later gated decision | Before any use of “certified,” accreditation work, or AI certification tooling |
| M-13 | W3C Verifiable Credentials Data Model 2.0, Recommendation 2025-05-15 | W3C Recommendation | Issuer–holder–verifier separation; status, security, privacy, accessibility, and purpose-aware disclosure concepts; cryptographic verifiability establishes authenticity/currency properties but does not establish the truth of encoded claims, which remain subject to verifier policy | No credential is issued in v0.1; signing and status infrastructure are planned, not implemented; a verifiable format cannot substitute for validity, factual verification, or an impartial issuance decision | On relevant Recommendation/errata changes |
| M-14 | 1EdTech Open Badges 3.0 implementation guide, Final page accessed 2026-08-31 | Industry specification and implementation guide | Future interoperability path for evidence-bearing credentials and conformance-tested implementation | No Open Badges conformance or certification claim; not active in v0.1 | Before credential implementation and at specification updates |
| M-15 | SFIA 9, current version accessed 2026-08-31 | Licensed professional skills framework | Separate responsibility/autonomy/complexity from named skills; contextual role profiles rather than one universal job shape | SFIA content is licensed; no copied mapping or endorsement; commercial/reproduction rights require review | Before any crosswalk or commercial use |
| M-16 | OWASP Top 10 for Agentic Applications 2026, reviewed 2026-08-31 | Community security guidance | Agent/evaluator threat modeling, prompt injection, tool boundaries, privilege, data exfiltration, and human-control requirements | Not a security certification and not a substitute for platform threat modeling/testing | On major revision and threat-model review |
| M-17 | NIST SP 800-63-4 Digital Identity Guidelines and SP 800-63A Identity Proofing and Enrollment, final Revision 4 reviewed 2026-08-31 | U.S. government technical guidance, written for digital government identity systems | Risk-based identity assurance; privacy assessment; data minimization; notice and redress; layered fraud management; continuous performance review; comparable paths | No federal assurance-level or conformance claim; controls require tailoring for a private employment-adjacent research service | Before identity-provider selection, proofing pilot, or material fraud-control change |
| M-18 | AAPOR Disclosure Standards and Transparency Initiative, current pages reviewed 2026-08-31 | Professional-association ethics, methods-disclosure, and transparency program | Disclose sponsor/conductor, instrument and scripts, population, sampling/recruitment, dates, mode/language, interview duration, interviewer/coder preparation, processing, and limitations for released interview research | LockedIn is not represented as an AAPOR member or Transparency Initiative participant; no audit or conformance claim; requirements are adapted beyond public-opinion research | Before each interview-method release and on AAPOR code/standard revision |
| M-19 | Tong, Sainsbury, and Craig, “Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups”, 2007 | Peer-reviewed qualitative-research reporting checklist | Research-team/reflexivity, study design, setting/sample, collection, analysis, and reporting prompts for interview publications | A reporting checklist is not evidence that a study was well designed, valid, representative, ethical, or conformant; no completed COREQ checklist or conformance claim exists | At protocol freeze and publication review |
| M-20 | O’Brien et al., “Standards for Reporting Qualitative Research: A Synthesis of Recommendations”, 2014 | Peer-reviewed qualitative-research reporting recommendations | Transparent problem formulation, purpose, approach, researcher characteristics/reflexivity, sampling, ethics, collection/analysis, trustworthiness, findings, limitations, funding, and conflicts | Reporting completeness does not establish methodological quality or validation; no SRQR conformance claim exists | At protocol freeze and publication review |
| M-21 | HHS 45 CFR part 46 and OHRP informed-consent guidance, current pages reviewed 2026-08-31 | U.S. federal regulation and regulator guidance for covered human-subjects research | Applicability review, prospective informed-consent principles, voluntariness, understandable disclosure, participant questions, withdrawal, and documented review authority | Applicability input only: this ledger does not determine that the commissioned interviews are regulated human-subjects research, grant an exemption, or imply IRB/OHRP approval | Before any real-record intake, recontact, prospective recruitment, or method change |
| M-22 | Krippendorff, “Reliability in Content Analysis: Some Common Misconceptions and Recommendations”, 2004 | Peer-reviewed methodology article | Code-level reliability, chance-adjusted agreement, uncertainty, and explicit action rules for human-coded interview material | Alpha thresholds are decision conventions, not universal facts; prevalence, unitization, sample size, confidence intervals, consequences, and alternative agreement evidence remain visible; no reliability result exists | At codebook freeze, reliability-plan review, and every coding-method change |
| M-23 | NIST AI 800-3 — Expanding the AI Evaluation Toolbox with Statistical Models, published 2026-02 | U.S. government measurement-science report | Explicit estimands, modeling assumptions, item/trial dependence, and uncertainty must accompany benchmark comparisons and generalization claims | Developed for AI-system evaluation; its models and results do not transfer to people or validate the experimental scorer | Before any statistical result, comparison, or uncertainty claim |
| M-24 | NIST AI 800-2 — Practices for Automated Benchmark Evaluations of Language Models, initial public draft reviewed 2026-08-31 | U.S. government initial public draft; not a final standard | Measurement-target definition, content/use alignment, test conditions, contamination, reproducibility, uncertainty, and reporting questions inform benchmark operations; when an objective compares AI with humans-only or humans assisted by AI, relevant baselines should be statistically robust, including sufficient human sample size and expertise | Applies to automated AI evaluation, not occupational assessment; draft status remains visible; it does not establish construct equivalence, measurement invariance, a human sample size, or conformance for the Shared Work Challenge Battery | On final publication or material draft revision |
| M-25 | MLCommons submission rules, inference rules, and audit guidelines, current repositories reviewed 2026-08-31 | Industry-consortium operating rules | Version-specific rules, structured result packages, environment metadata, compliance checks, review, reproducibility, audit, correction, and verified/provisional/unverified status separation | Hardware/AI benchmark rules do not validate human assessment, and LockedIn has no equivalent independent audit operation | Before defining submission, verification, or independent-reproduction claims |
| M-26 | U.S. Census Bureau, Appendix A2: Questionnaire Testing and Evaluation Methods for Censuses and Surveys and Statistical Quality Standard A2, current pages reviewed 2026-09-01 | U.S. government statistical-quality guidance | Cognitive interviewing, questionnaire pretesting, respondent comprehension, mode effects, burden, usability, debriefing, and field-test evidence inform the proposed survey instrument lifecycle | Census standards do not approve LockedIn instruments or establish validity; planned surveys remain untested and unfielded | Before instrument freeze, after material item changes, and before a new collection mode |
| M-27 | NIH Office of Behavioral and Social Sciences Research, Best Practices for Mixed Methods Research, current resource reviewed 2026-09-01 | U.S. government-sponsored mixed-methods guidance | Intentional integration of qualitative interviews, quantitative surveys, work artifacts, rater observations, and outcome records informs the multi-cohort design and joint-display plan | Health-science guidance is adapted to occupational research; no NIH review, endorsement, funding, or conformance claim | At protocol freeze and mixed-method integration review |
| M-28 | Stanford CRFM HELM repository, versioned capabilities leaderboard, and reproduction guide, reviewed 2026-09-01 | Academic benchmark framework and versioned public results | Declarative run configurations, broad scenario coverage with explicit omissions, multi-metric measurement, controlled scenario adaptation, prompt-level auditability, reproduction commands, changelog, and coverage limits inform the Shared Work Challenge Battery and release packaging | HELM evaluates models, not people; its scores, adaptation choices, and scenarios do not validate FDE measurement or human/model score comparability, and its 2026 maintenance posture makes it a pattern rather than a runtime dependency | Before release-manifest design changes and on material HELM status changes |
| M-29 | Princeton NLP SWE-bench repository, evaluation guide, and submission artifact repository, reviewed 2026-09-01 | Academic benchmark, evaluation harness, dataset, and public submission record | Containerized runs, pinned revisions, per-instance logs and reports, pass@1 disclosure, scaffold/model separation, public re-grading, known-issue tracking, and optional verification reruns inform applied-task operations | Static public tasks carry contamination and defect risks; repository issue resolution is not evidence of human job performance, and no SWE-bench result is imported | Before controlled-case, leakage, rerun, or applied-task release decisions |
| M-30 | NIST Technical Note 1297 measurement uncertainty guidance and reporting guidance, reviewed 2026-09-01 | U.S. government measurement guidance | Identifying uncertainty components, evaluation method, combined and expanded uncertainty, coverage basis, and reporting conditions informs the future result-card contract | TN 1297 addresses NIST measurement results, not employment assessment; it does not prescribe or validate this benchmark's human measurement model | Before any calibrated score, interval, comparison, or uncertainty claim |
| M-31 | Institute for Credentialing Excellence / NCCA accreditation resources and FAQ, current pages reviewed 2026-09-01 | First-party professional credentialing and accreditation-program guidance | Governance autonomy, protection from undue influence, stakeholder/public-interest representation, separation of certification and education, and the distinction between separate incorporation and protected decision autonomy inform the institutional firewall | No NCCA conformance, eligibility, application, accreditation, or endorsement claim; the project’s external-majority rule is its own safeguard, not attributed to NCCA | Before any credential, certification, governance-autonomy, separate-entity, or accreditation decision and on NCCA standards revision |
Occupational assessment and validity sources
These sources are the primary methodological spine for a benchmark of people. They take precedence over AI-benchmark precedents when the design question concerns job analysis, assessment interpretation, raters, fairness, employment use, or certification.
| ID | Source and state reviewed | Authority / status | Material influence on this draft | Explicit boundary or non-adoption | Review trigger |
|---|---|---|---|---|---|
| V-01 | American Educational Research Association, American Psychological Association, and National Council on Measurement in Education, Standards for Educational and Psychological Testing, 2014 edition | Joint professional testing standards | Validity evidence is evaluated for each proposed score interpretation and use; job/practice analysis, content and response-process evidence, internal structure, relationships to other variables, reliability/precision, fairness, credentialing decisions, documentation, and test-user responsibility inform the program | Reviewed as a design foundation; no conformance, completed validity argument, validated psychometric instrument, or authorized score interpretation exists | On revision and before every validation or release decision |
| V-02 | Society for Industrial and Organizational Psychology, Principles for the Validation and Use of Personnel Selection Procedures, 5th edition 2018, DOI 10.1017/iop.2018.195 | Professional personnel-selection principles | Work analysis, intended-use validity evidence, representative samples, criterion quality, rater evidence, subgroup analysis, documentation, and replication expectations | No selection procedure has been validated; no SIOP endorsement or conformance claim | On revision and at job-analysis, pilot, and technical-manual review |
| V-03 | Roth, Bobko, and McFarland, “A Meta-Analysis of Work Sample Test Validity”, 2005 | Peer-reviewed meta-analysis | Supports direct study of job-representative work samples while warning against treating the format as infallible | No published coefficient is imported; local task, population, rater, and criterion evidence is required | At content-blueprint and criterion-study design |
| V-04 | Sackett et al., “Revisiting Meta-Analytic Estimates of Validity in Personnel Selection”, 2022 | Peer-reviewed meta-analytic reassessment | Reinforces that familiar validity estimates can be overstated and cannot become proprietary weights or claims without local evidence | No external coefficient establishes this benchmark’s validity | At calibration, criterion-validation, and public-claim review |
| V-05 | Mislevy, Almond, and Lukas, “A Brief Introduction to Evidence-Centered Design”, 2003 | ETS research report / assessment-design foundation | Explicit chain from claims about capability to evidence and task design informs the proposed content-blueprint structure | Educational-assessment models do not validate this occupational construct or require a specific psychometric model | At content-blueprint and instrument-design review |
| V-06 | Kane, “Validating the Interpretations and Uses of Test Scores”, 2013 | Peer-reviewed argument-based validity framework | Proposed interpretation-and-use arguments, assumptions, warrants, rebuttals, and evidence gaps inform the validation register | A written argument is not itself validity evidence; each inference remains empirical | At protocol freeze, technical-manual review, and use expansion |
| V-07 | Campion, Palmer, and Campion, “A Review of Structure in the Selection Interview”, 1997 | Peer-reviewed review | Job-related fixed questions, consistent administration and probing, anchored scales, and evaluator structure inform the proposed evidence defense | Interview research does not validate a live defense or permit unstructured reviewer discretion | At reviewer-protocol and rater-study design |
| V-08 | LeBreton and Senter, “Answers to 20 Questions About Interrater Reliability and Interrater Agreement”, 2008 | Peer-reviewed methods article | Reliability and agreement answer different questions; statistic, scale, decision, and aggregation rule must match | No universal threshold is adopted and no rater result exists | At rater-analysis plan and every scale change |
| V-09 | U.S. Office of Personnel Management, Job Analysis, current page reviewed 2026-08-31 | U.S. government assessment-practice guidance | Task/competency analysis and linkage to assessment content inform the proposed practice-analysis and blueprint artifacts | Guidance is not a federal approval or validation of this private benchmark | At job-analysis protocol and content review |
| V-10 | U.S. Office of Personnel Management, Reference Checking, current page reviewed 2026-08-31 | U.S. government assessment-practice guidance | Job-related common questions and standardized ratings support treating references as structured corroboration in a multi-method process | References do not prove identity or competence and cannot replace direct work evidence | Before reference-pilot design or use-policy change |
Market landscape sources
These are dated product observations, not independent quality judgments. The existence of these offerings means LockedIn must not use “first,” “only,” or “category creator” without fresh substantiation.
| ID | Offering reviewed | Observed public posture at cutoff | Strategic lesson; no comparative claim |
|---|---|---|---|
| C-01 | FDE Compass | Career diagnostic using 24 engineering/scenario questions across six axes, plus job-description analysis and a personalized plan; public page displays a readiness number | Self-report/scenario readiness and career guidance already exist. LockedIn’s defensible boundary is governed evidence, uncertainty, provenance, and independent review—not generic readiness language. |
| C-02 | GSDC Certified Forward Deployed Engineer | Training-linked certification; public exam details list 40 multiple-choice questions, 90 minutes, 65% pass, five-year validity, and a complimentary retake | Knowledge testing and course-linked credentials already exist. LockedIn must separate training economics from measurement/governance and show what field evidence adds. |
| C-03 | Good Combinator FDE Certification Program | Eight-course, approximately 130-hour paid program described as evidence-based/mastery-graded, awarding a verifiable FDE certification | Evidence and verifiable-credential language are already in market. LockedIn must substantiate its precise evidence definitions, independence, and validity rather than compete on adjectives. |
| C-04 | Artificial Analysis | Independent benchmarking company spanning models, agents, inference, systems, and a high-cadence publication/product surface | The benchmark is operating depth: methods, current data, analysis, archive, and utilities as one system. LockedIn needs equivalent research discipline before it can claim equivalent authority. |
| C-05 | FDE Arena public artifact and Zonko Labs retrospective | The public artifact shows a simulated customer workspace and transcript-based score. Its creator’s retrospective says 120 candidates attempted, none completed, and the product was no longer treated as an active bet. | Simulation/report-card concepts have been tested in market, and completion burden is a material design risk. Do not list FDE Arena as an active competitor without a fresh operating-status check. |
See Market Landscape — 2026-08-31 for the dated strategic analysis.
Legal and high-consequence monitoring sources
| ID | Primary source | Why it is monitored | Current product response | Required authority |
|---|---|---|---|---|
| L-01 | New York City DCWP — Automated Employment Decision Tools | The page describes bias-audit, public-summary, and candidate/employee notice duties for covered AEDT use | v0.1 prohibits automated employment decisions; future employer use needs jurisdiction/use-case analysis, bias-audit design, notice, alternatives, and audit-publication operations | Employment/privacy counsel; independent bias-audit authority where applicable |
| L-02 | European Commission — AI Act framework and high-risk guidance/timeline | The Commission includes employment among Annex III high-risk areas and, following the 2026 AI Omnibus, states that those rules apply from 2027-12-02; risk, data, documentation, logging, human-oversight, robustness, security, and accuracy duties are described | Do not market a score as a hiring decision; build purpose, oversight, documentation, quality, appeal, and monitoring controls before EU use; monitor final guidance and deployment-specific classification | EU AI/privacy/employment counsel and conformity specialists |
| L-03 | Colorado Attorney General — ADMT rulemaking | At the cutoff, the revised ADMT law and proposed rules were moving toward a 2027 effective date, with correction and anti-discrimination duties | Treat applicability and final requirements as moving; maintain change monitoring; no Colorado compliance claim | Colorado counsel after final rules and per deployment |
| L-04 | Illinois Artificial Intelligence Video Interview Act | Covers specified AI analysis of applicant video, including notice, explanation, consent, sharing, and deletion duties | v0.1 has no video-interview analysis; any future modality requires a fresh legal and measurement review | Illinois employment/privacy counsel |
| L-05 | EUR-Lex — GDPR consolidated text, Article 22 and applicable official EU guidance | Automated decisions and profiling can trigger significant rights and safeguards | Human review must be substantive; consent/purpose/data rights and contestability are architectural, not checkbox features | EU privacy counsel and data protection officer |
| L-06 | EEOC — Employment Tests and Selection Procedures and EEOC AI/ADA resources | Federal anti-discrimination law can apply to employment tests and AI-assisted applicant/employee assessment; job relevance, validation, accommodation, and avoiding disability screen-out are central concerns | Keep public v0.1 outside employment decisions; future use requires job-related validation, effective alternatives/accommodations, monitoring, and employer/vendor responsibility analysis | U.S. employment/civil-rights counsel; industrial-organizational/measurement and accessibility specialists |
| L-06B | EEOC — Questions and Answers on the Uniform Guidelines on Employee Selection Procedures and 29 CFR Part 1607 | Official guidance and federal guidelines address job analysis, important work behaviors and products, standardized administration, validity strategies, records, cutoffs, adverse impact, and alternatives | Content resemblance, labels, testimonials, or vendor credentials do not substitute for use-specific validity evidence; applicability and legal sufficiency require counsel | U.S. employment/civil-rights counsel and qualified personnel-selection/measurement specialists |
| L-07 | U.S. Department of Justice — Algorithms, AI, and Disability Discrimination in Hiring | DOJ guidance explains that hiring technology can unlawfully measure disability rather than job skill and describes accessible alternatives/accommodations | Accessibility and alternative demonstration are measurement requirements, not post-hoc interface fixes | ADA/employment counsel and independent accessibility specialist |
| L-08 | FTC Policy Statement Regarding Advertising Substantiation | Objective claims about research amount or strength can communicate that the advertiser possesses corresponding support before dissemination | Hold interview counts and “research-backed” language until the exact claim reproduces from an approved evidence package; review implied as well as literal meaning | U.S. advertising/consumer-protection counsel for public campaign wording; this ledger is not legal advice |
The legal table is deliberately non-exhaustive. U.S. federal and state employment, civil-rights, consumer-protection, biometric, accessibility, privacy, recordkeeping, and professional-credential requirements can apply independently of AI-specific laws.
Adoption-state vocabulary
Every future crosswalk must use one of these states:
| State | Meaning |
|---|---|
reviewed |
Source was read and may have informed questions or terminology. |
informed |
A documented design decision was influenced by the source. |
mapped |
A clause/requirement-level comparison exists, including gaps and departures. |
implemented |
The mapped control exists in a named release and has owner evidence. |
tested |
A defined test passed against that implementation. |
independently_reviewed |
An authorized party outside the implementation line reviewed the evidence. |
conformant |
The governing source permits the claim and the designated authority approved it. |
The current ledger records only reviewed and informed states.
Maintenance and citation process
- The Research Director owns source selection; Legal owns legal applicability; the Measurement Lead owns construct and method mappings; the Standards/Licensing owner records permissions.
- Automated link checks can flag availability changes but do not establish continuing accuracy.
- Before a release, each material source receives a fresh access date, version/status check, and impact review.
- Changed or withdrawn sources open a decision record. Published benchmark releases remain immutable; corrections or method changes create a new release.
- Every public research claim must link to a frozen source set, method version, analysis code/data availability statement, limitations, conflicts/funding disclosure, and correction path.
- Sources that cannot legally be reproduced may inform a private mapping but cannot be copied into the public ontology.
Known gaps
- No clause-level standards conformance mapping exists.
- No independent measurement, legal, privacy, accessibility, or licensing review has approved this ledger.
- No systematic-review protocol or evidence-quality grading has been executed.
- Market coverage is non-exhaustive and may change rapidly.
- No field study, criterion-validity study, reliability study, fairness study, or longitudinal outcomes dataset exists.
These gaps block validation and certification claims; they do not block transparent review of the proposed framework.