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About ctzn.pub

A publishing platform that cannot make up a number.

ctzn.pub computes statistics from rigorous public surveys and administrative data — GSS, ANES, CES, CDC, Census, and more — publishes them as interactive essays, and ships the same computations as machine-readable claims. Every figure survey-weighted. Every claim sourced.

From raw data to a published claim

Four stages, the same for every article on the site.

Public sources

Public survey and administrative microdata — GSS, ANES, CES, YRBS, MTF, SCF, CDC PLACES, Census/ACS and more — pulled from the canonical source and pinned to a versioned extract, not a live query.

Design-correct computation

Estimates computed the way the survey’s own methodology demands: the right weights, replicate variance, small-cell suppression — applied, not approximated.

Interactive essays

Computed figures become interactive charts and maps, woven into long-form essays that explain what the data shows and why it matters. Source cited on every figure.

Machine-readable surfaces

Published articles ship as clean Markdown mirrors, a claims feed, and structured data an AI engine can cite. A full claims API and MCP server are in development.

Peer review, built into the machine

Before anything is computed at scale from a new source, we write down the full analysis plan — which estimates get precomputed, under which survey weights, with which demographic cuts, and what gets suppressed — and render it into a single readable document. A human statistician reviews the statistics in that plan: the survey-design handling, the cuts, the cell-size rules, the exact wording of what we'd claim.

The review is binding, not a courtesy read. The approval is tied to a fingerprint of the exact plan a reviewer read — edit the plan afterward, and the fingerprint breaks, reopening review. Small cells are pooled and the pooled range is labeled; we never lower a suppression floor to make a cut work. Series breaks — a survey redesign, a mode change, a weight splice — are shown as breaks, never drawn through as one continuous line.

One design mistake would replicate across thousands of published numbers. So a person signs every plan before a single number is computed.

There is deliberately a human at the other end of every one of these.

What we won't compromise on

Four standing rules, not marketing.

Academically rigorous
Every figure is survey-weighted and every claim traces back to its exact source — no vibes, no invented numbers.
Pinned to a snapshot
Every figure is pinned to a versioned data extract, not a live query that can drift under you. We refresh the pin when a source publishes a new wave, and every article states its vintage.
Agent-ready surfaces
Markdown mirrors, a machine-readable claims feed, and JSON-LD — the surfaces AI engines cite today.
Built to read
Interactive charts and maps designed for long-form reading — built for researchers and journalists first, machine-readable second.

Who runs this

ctzn.pub is built and run by a small team working directly with the survey methodology — the same people who compute the figures, write the essays, and review every published number.

Contact

Questions about a figure, a correction, a licensing question, or interested in the institutional library — reach us directly.

info@ctzn.pub
For agents & builders

Machine surfaces

What machines can read today: Markdown mirrors of every published article, a machine-readable claims feed, and structured data on every article page. No key required — these are public, crawlable surfaces.

Markdown mirrors

Every published article is also served as clean, extraction-friendly Markdown — prose intact, each chart replaced by its sourced takeaway.

GET /api/md/<username>/<slug>

Claims feed

A machine-readable feed of sourced findings from published articles — the numbers, their source surveys, and the article they came from.

GET /claims.json

Structured data

Published articles carry Schema.org JSON-LD (Article + Dataset), so search and answer engines can attribute every figure.

application/ld+json on article pages

Open to AI crawlers

robots.txt explicitly welcomes GPTBot, ClaudeBot, PerplexityBot and peers across all public content.

GET /robots.txt
claims.json — live example
{
  "generated": "2026-07-03T22:14:36.012Z",
  "count": 2,
  "claims": [
    {
      "article": "The widening mental health gap among US teens",
      "url": "https://uma.ctzn.pub/the-widening-mental-health-gap-among-us-teens-wyl28",
      "title": "Poor Mental Health Among US High School Students",
      "source": "YRBS",
      "takeaway": "Female: 40.8 (2021) → 38.8 (2023), -2.0; Male: 18.1 (2021) → 18.8 (2023), +0.8."
    }
  ]
}

On the roadmap

A full public REST API and MCP server over the article and claims catalog is in development and not yet available. Nothing on this page requires a key or a plan — when a metered API ships, it will be documented here first.