CensusBA-D-2026-052026
AI crawlers that supply answer engines largely do not execute JavaScript, so the text they read is what the raw HTML holds before any script runs. This census measures how much of a university homepage's visible text exists only after client-side rendering. For the 429 analyzable homepages of companion census BA-D-2026-02, it compares the stored raw HTML's visible text (what a non-rendering crawler saw on 2026-07-09) against document.body.innerText from a headless-Chrome visit to the same URL (2026-07-10). Of the 400 that rendered cleanly, 354 (88.5%) were within 10% of their rendered text and only 16 (4.0%) at least doubled it; just 6 (1.5%) were hard walls, near-empty without JavaScript. Decomposing those six shows only two are genuine client-side rendering — the rest are bot-challenge shells or JavaScript redirects — so a 'universities are single-page apps' reading is unsupported. Findings span two adjacent days and should be assumed perishable.
CensusBA-D-2026-032026
On 2026-07-10 the server-delivered homepage HTML of 2,000 domains — four documented frames of 500 universities, news outlets, e-commerce sites, and U.S. federal government domains, the same frames as the crawler-access census BA-D-2026-01 — was parsed for JSON-LD, Microdata, and RDFa markup. Every signal is read from the raw, unrendered HTML a non-rendering crawler receives; JSON-LD injected by client-side script is invisible by design. Among each frame's analyzable homepages, JSON-LD ranged widely: 80.6% (333 of 413) of news, 56.6% (151 of 267) of e-commerce, 33.6% (144 of 429) of universities, and 19.1% (63 of 329) of government. Where present, the dominant types are generic — WebSite, Organization, WebPage — not the entity's own kind. It measures deployment only: presence of markup, not consistency across pages or languages, nor whether marked-up facts appear in visible text. Non-response is reported separately; university bytes are reused from 2026-07-09, the rest fetched 2026-07-10.
WhitepaperBA-W-2026-042026
Commercial AI-visibility tools have converged on a recognizable form: a daily score per tracked query, a trend line of those scores, a cited-or-not verdict per topic, and per-engine coverage badges. This paper argues the genre, as a class, cannot meet the disclosure floor a measurement requires. Walked against the ten minimum-disclosure items of BA-C-4, the common form omits the load-bearing ones: a daily single run is one Bernoulli draw per query, so the score carries no usable sample size; a line of bare points hides the day-to-day drift that is its own variance; one frozen query per topic measures the wording, not the need; API collection cannot support a claim about what users see; refusals and engine-wide updates go unmarked. The paper states what a conforming tool would show instead, and closes with five questions a buyer can ask. No tool is named or assessed.
CensusBA-D-2026-042026
On 2026-07-10 the Common Crawl index for CC-MAIN-2026-25 — the June 2026 monthly crawl — was queried for 2,000 domains: the four documented frames of 500 universities, news outlets, e-commerce sites, and U.S. federal government domains from crawler-access census BA-D-2026-01. Coverage is reported two ways, because 'is a domain in Common Crawl' has two operationalizations that disagree. Captured — at least one archived record with HTTP 200 or a revisit — holds for 89.0% (1,781 of 2,000); presence — at least one index record of any status — for 95.3% (1,907). Universities are wall-to-wall (100%); news is lowest (82.8% captured, 69 fully absent). Joined to the same domains' robots.txt policy, 354 that root-block CCBot still appear in the June crawl — a timing relationship, not a compliance finding. Presence at this pipeline's front door implies nothing about a model retaining or reproducing the content.
CensusBA-D-2026-022026
University homepages compete for internationally mobile students who increasingly research destinations through AI assistants, yet the international-targeting layer that tells a crawler which language and regional variants of a page exist is frequently absent at the front door. This census measures that layer directly. Using the raw, unrendered HTML served to a non-JavaScript crawler, it examines the homepages of a frame of 500 universities — the 300 largest U.S. institutions by enrollment plus 200 international universities by traffic rank — of which 429 returned an analyzable page on 2026-07-09. In that HTML, 84.1% (361 of 429) declared no hreflang alternates at all. Among the 68 that did, quality was high: no invalid language codes and 89.7% carrying a self-reference. Canonical and language-tag hygiene showed the same adoption-not-correctness pattern. Findings describe raw HTML on a single day; sites change without notice, and results should be assumed perishable.
ReportBA-R-2026-022026
The companion evidence review (BA-R-2026-01) established that a large and rising share of prospective students consult AI assistants when deciding where to study, and that, to this publication's knowledge as of July 2026, no measured account of which institutions and services those assistants actually surface has been published. This document is the study protocol for the first measured edition: it pre-registers, before data collection, the estimands, the cell design, the sample-size targets and interval methods, the measurement channels and their calibration, the pooling and multiplicity treatment, the refusal handling, the entity-naming policy, and the criteria under which any change will be claimed. The protocol discloses the statistical design layer in full and withholds the operational layer — concrete phrasings and tooling — per the disclosure floor of BA-C-4. Deviations in the eventual edition will be disclosed against this document. No publication date is promised; the edition follows the data.
WhitepaperBA-W-2026-032026
A web page is read by four kinds of machines: search crawlers, the retrieval pipelines behind AI answer engines, assistive technology, and autonomous browser agents. Three practices — search engine optimization, generative engine optimization (GEO), and web accessibility — each optimize for one or more of these readers. Working from primary documentation, specifications, regulations, peer-reviewed studies, and, for one 2022 statement lacking an official transcript, a flagged trade-press transcription (sampled 2026-07-09), this paper states each practice as a reader–objective–evaluator tuple, maps twelve page-level signals against the four readers, and audits the circulating claim that accessibility improves AI visibility. Five signals have documented consumers in multiple reader categories. But both load-bearing links of the proposed accessibility-to-GEO mechanism are verified documentary absences, Google states accessibility is not a direct ranking factor, and agent systems split between accessibility-tree and screenshot perception. It closes with five falsifiable propositions that no public study yet measures.
CensusBA-D-2026-012026
On 2026-07-09 the robots.txt policy of 2,000 domains — four documented frames of 500 universities, news outlets, e-commerce sites, and U.S. federal government domains, each built from a cited public source and ordered by a public ranking (Tranco traffic rank, or reported enrollment for the U.S. universities) — was fetched and evaluated against sixteen crawler tokens. Among domains with a determinable policy, news sites root-blocked AI crawlers far more than any other sector: 66.7% (288/432) blocked at least one AI-specific token, against 7.8% (33/423) of universities. A distinct pattern recurs in news: 45.8% (198/432) root-blocked a retrieval-class crawler that supplies AI answer engines while staying open to general search. Non-response was itself a finding — the policy was undeterminable for 40.6% (203/500) of government and 37.8% (189/500) of e-commerce domains. This census enumerates the four frames completely; it makes no sampling inference to all universities, news sites, stores, or agencies.
WhitepaperBA-W-2026-022026
When a person asks an AI assistant a question — which schools to consider, which vendor to trust, which clinic to visit — the answer names some organizations and omits others. Being named is AI visibility, and it is not the same as ranking on a search results page. This introduction defines the phenomenon and explains why it must be measured as a probability rather than checked once. It distinguishes the two ways an entity becomes known to an assistant — live retrieval of sources at answer time, and parametric memory formed during training — and summarizes the public evidence that answers are unstable: rewording a question slightly, or asking it again tomorrow, changes the sources and the names returned. It introduces three metrics — Visibility Probability, Share of Voice, and Discovery Depth — and the Barkhausen Ladder, the field's maturity map, pointing to the conventions for formal definitions.
WhitepaperBA-W-2026-012026
A brand's visibility in AI assistants is routinely 'verified' with a single screenshot or one daily query. This paper argues such verification is not measurement. Answer engines are stochastic and their retrieval changes continuously: lightly rewording a query while holding its intent fixed cut the overlap of the brands an assistant recommended to a Jaccard similarity near 0.3 — far below the 0.50–0.61 overlap of a plain re-run — and an identical prompt re-issued a day later overlapped only 34–42% in cited sources and 45–59% in mentioned brands. A single observation of a moving distribution estimates nothing. The paper enumerates what voids a visibility claim — no sample size, no interval, no window, no engine version, one fixed phrasing, uncontrolled personalization, discarded refusals — and shows a three-sigma jump can be pure drift. It then states what valid measurement requires — repeated sampling to a declared precision, bounded intervals near the extremes, a phrasing distribution, partial pooling, explicit windows and versions, change-point monitoring, recorded refusals — specified in BA-C-2 and BA-C-3.
ReportBA-R-2026-012026
Prospective students and their families increasingly consult AI assistants when deciding where to study. This report reviews the public evidence — surveys, web-analytics studies, and query-monitoring analyses published between 2023 and 2026 — on the adoption of AI assistants in education and study-abroad decisions. Across independent studies in the United States, the United Kingdom, and among international students, the pattern is consistent: a steep, replicated adoption curve. In the United States, the share of high-school seniors using AI to explore colleges rose from 4% (2023) to 23% (2025). In a UCAS survey of 4,485 prospective UK applicants (November 2025), 48% had used AI to explore their university options. Among 1,622 newly-enrolled international students surveyed in the US and UK in September 2025, 17% used AI in their initial school research. Every figure is reported with its source and, where disclosed, its sample and window; the known bias toward vendor-published data is stated openly. The evidence establishes that visibility in AI answers is now both consequential and measurable for education institutions.
