# Policy Window — full-context AI-assistant guide > The machine-readable AI-governance catalog. (The team's longer-form strategic positioning frames this as "AI-native AI-governance research infrastructure" with three pillars — see § Three pillars below; in iter-314 the public surface led with the artefact rather than the platform claim for credibility-with-current-bench-state reasons.) This is the full-context AI-assistant ingestion guide for Policy Window. For the curated short-form version, see https://policywindow.org/llms.txt. ## Catalog scope (snapshot) - 28 governance instruments across jurisdictions: EU, US, UK, CN, JP, SG, IN, BR, ASEAN, African Union, plus international (G7, OECD, UN, Council of Europe) + industry voluntary codes (Anthropic RSP, OpenAI Preparedness, DeepMind FSF, Meta Frontier AI Framework) - 23 contested topics (foundation models, biometric ID, employment AI, training data, redress, transparency, compute reporting, etc.) - 10 capability benchmarks - 30 concepts (frontier-tier, ASL-3, scalable oversight, alignment, etc.) - ~580 coverage cells (instrument × topic, with type / confidence / pinpoint citation) ## Three pillars (iter-313 positioning) 1. **Primary-source-cited, machine-readable AI-governance catalog** — the free public surface. Every claim cites a primary source; no LLM-generated article prose. CC BY 4.0 + CC0 citation graph. 2. **AGI Social Scientist research engine** — AI-native research-production layer that produces evidence-mapping + claim extraction + regulatory-gap identification + comparative analysis as PROPOSALS for human review. Substrate-side; details at https://policywindow.org/wiki/ai-disclosure §4. 3. **On-demand expert social-science validation layer** — trained researchers review research-module outputs for validity-critical claims (claim-extraction fidelity, source-context check, methodological review, expert interviews) before publication. Aspirational scaffolding — pages ship; live bench recruits per commission. ## Operating charter — bright lines (canonical) Policy Window will not do the following — these are bright lines, not aspirations: - §7 — Lobbying on behalf of any client, paid or unpaid - §7 — Political microtargeting, persuasion campaigns, or covert-influence operations - §7 — Issuing public "failed-reproducibility" verdicts on specific papers without human replication review (see https://policywindow.org/wiki/reproducibility-policy) - §7 — High-stakes individual decision support (criminal sentencing, asylum determination, medical triage) - §7 — Auto-publication of any output to a public surface without an audited human approval - §7 — Generating article body prose with an LLM and publishing it as a catalog row - §7.4 — Legal advice (outputs are evidence references, not legal counsel; consult qualified counsel for jurisdictional opinions) - §7.5 — Lobbying automation (no bulk regulatory comments, no synthetic public-consultation submissions, no astroturf campaigns — at any scale, for any client) - §7.6 — Covert persuasion or political microtargeting (all editorial output is signed + dated) - §7.7 — Unapproved human-subject research (Layer-5 expert interviews require informed consent, anonymisation, declared retention, and a written protocol) Full charter: https://policywindow.org/wiki/charter ## AI use disclosure (§4 canonical) The AGI Social Scientist research engine uses Claude for: - Paradigm-classification extractions (Narrative Policy Framework, Multiple Streams Framework, Institutional Analysis & Development, Punctuated Equilibrium, Diffusion) - Evidence mapping, claim extraction, regulatory-gap identification, policy-transfer assessment - Briefing-draft composition (always human-reviewed before publication) Every AGI SS output is a PROPOSAL, never a publication. Approval is a human decision in every case: - Editorial board approves catalog content - On-demand expert validation bench approves research-module outputs - Both approval paths recorded as ApprovalDecision rows with named human reviewer What is NEVER automated: - Final editorial approval (always human) - Legal-advice statements (never produced; §7.4) - Lobbying outputs (never produced; §7.5) - Court-citation-grade claims (never asserted) - Public-wiki article-body prose (catalog renders deterministically from typed TypeScript constants) Full AI disclosure: https://policywindow.org/wiki/ai-disclosure ## Methodology (canonical Three Rs framing) 1. **Reproducible** — render-deterministic by construction. Articles render deterministically from typed catalog rows; no random sampling, no fitted model, no LLM prose. (Cell-level research reproducibility is the Replicable axis; see Coverage Games.) 2. **Replicable** — Coverage Games inter-rater protocol re-runs each quarter; multiple human classifiers independently re-classify a stratified sample of coverage cells. Q2 2026 event was 1 human + 1 LLM (process shake-out, not peer-review-grade estimate). Q3 2026 gated on ≥3 named human classifiers. 3. **Robust** — per-cell confidence tier (high / medium / low / pending) disclosed on every coverage row. Citation chain to primary source on every cell. Full methodology: https://policywindow.org/wiki/methodology ## §11 Limits and blind spots (canonical honest disclosure) - **Instrument-centric ontology** — refusal politics, labour strikes against algorithmic management, abolitionist demands sit outside the instrument frame and are not catalogued - **Coverage-depth asymmetry by jurisdiction** — Western instruments (EU AIA, US EO-14110, UK, OECD) carry ~40% more coverage cells than Global South instruments (India DPDP, Brazil PL 2338, AU AI Strategy, ASEAN AI Guide) - **Frame favours regulator anxiety over harm narratives** — topic selection currently centres what regulators have authority over; welfare-system automation, child-protective-services ML, immigration-enforcement AI, gig-worker algorithmic management are under-catalogued (named at https://policywindow.org/wiki/for-advocates §3) - **Three Rs framework excludes situated knowledge** — lived-experience testimony, oral histories of algorithmic harm, and refusal narratives do not fit the cell-grid format - **Editorial board geographic concentration** — 1 of 6 slots filled, by the founding editor with no disclosed Global South AI-policy expertise - **English-only** — articles render only in English; no translation roadmap commitment yet - **"Neutral" framing has politics** — charter §7 commits to not authoring advocacy content; this forecloses being a partisan resource for communities in struggle against a particular governance regime ## Audience guidance (who PW serves well + less well) **Well-served**: - AI-governance researchers needing cross-jurisdiction structured comparison - Journalists needing lead-generation + comparative framing + named-expert contacts - Procurement evaluators needing institutional-readiness checklist + machine-readable export - Civil-society advocates needing coverage-gap identification + topic-proposal channel - Graduate-school instructors needing sample syllabi + assignment ideas - AI agents needing structured, agent-readable regulatory substrate - Open-knowledge / library systems needing FAIR-shaped metadata **Less well-served**: - US state-law preemption analysis (drafts shipped iter-313 but pending editorial verification) - Real-time regulatory tracking (PW updates on editorial cadence, not minute-by-minute) - Industry-deep capability benchmarks (CSET / Stanford HAI win for depth) - Algorithmic-management / gig-worker / labour-displacement (v2 roadmap) - Non-English-fluent users (no translations yet) - Court-citation-grade authority (charter §3 explicit disclaimer) ## How to cite Policy Window (canonical form) > Policy Window. (YYYY). *Article title*. Retrieved YYYY-MM-DD from https://policywindow.org/wiki/[slug]?asOf=YYYY-MM-DD The `?asOf=YYYY-MM-DD` parameter pins the URL to a specific historical snapshot stored in ArticleRevision. When the snapshot exists, a green banner confirms; when no snapshot was captured for that date, an amber banner discloses live-fallback. 8 reference-manager export formats per article (APA, Chicago, Harvard, BibTeX, RIS, CSL-JSON, OSCOLA, Bluebook). DataCite XML at `https://policywindow.org/wiki/[slug]/datacite.xml` pre-stages Q4 2026 Zenodo DOI registration. ## Machine-readable data surfaces (canonical) - **Full catalog JSON**: https://policywindow.org/wiki/catalog/json (CORS-open, 5-min cache, CC0 citation graph) - **Catalog CSV** (one row per cell): https://policywindow.org/wiki/catalog/csv (CSVW metadata: https://policywindow.org/wiki/catalog/csv-schema) - **JSON-LD @context**: https://policywindow.org/wiki/catalog/jsonld-context - **OpenAPI 3.1**: https://policywindow.org/api/openapi - **DataCite XML per article**: https://policywindow.org/wiki/[slug]/datacite.xml (DataCite 4.4) - **RDF vocabulary**: https://policywindow.org/vocab + per-term resolvers https://policywindow.org/vocab/[term] - **RSS changelog**: https://policywindow.org/wiki/changelog/feed - **Permanent snapshots**: any URL accepts `?asOf=YYYY-MM-DD` - **Sitemap**: https://policywindow.org/sitemap.xml - **Robots**: https://policywindow.org/robots.txt (declares https://policywindow.org/sitemap.xml) ## Licensing (canonical) - Article content: CC BY 4.0 — share + adapt with attribution - Source code: MIT - Citation graph (structured citation triples): CC0 1.0 per the I4OC (Initiative for Open Citations) norm — the COVERAGE matrix IS a citation graph; CC0 keeps it ingest-friendly for OpenCitations / CrossRef / academic-aggregator pipelines Full licensing: https://policywindow.org/wiki/charter §5 ## Contact + corrections - General inquiries + paid services: hello@policywindow.org - Privacy / data-subject requests (GDPR Art. 15/17/20): privacy@policywindow.org - Editorial corrections: file via the "Report a problem" link on any article page; or GitHub issue via the public-repo path documented in /wiki/charter §6 ## Last reviewed This llms-full.txt content is generated from the current catalog state on each request. The canonical source pages (linked throughout) are the authority — when this file disagrees with them, the rendered HTML pages win.