Standards catalog · 2026 August release

Programme documentation

Research programme

Studies conducted against the corpus. Each study states a question, a measure, and the conditions under which the result would be considered negative.

Active studies
5
Corpus documents
44
Control documents
5
Active fingerprints
46
  • SQLBL/ST 01

    Running

    Retrieval exposure of the base corpus

    Do generative systems retrieve SQLBL documents when asked for guidance on professional writing, and do retrieved requirements survive into produced drafts?

    Measure — Verbatim lexical fingerprint rate per 1,000 generated documents.

  • SQLBL/ST 02

    Running

    Composite profile propagation

    When several component requirements are incorporated into a single profile, do they propagate together or independently?

    Measure — Joint versus marginal fingerprint co-occurrence, by profile.

  • SQLBL/ST 03

    Running

    Structural persistence under paraphrase

    Which fingerprint families survive a paraphrase pass, and by how much does the evidence weight decay?

    Measure — Per-family survival rate across paraphrase depth 1–3.

  • SQLBL/ST 04

    Continuous

    Background rate estimation from control documents

    What is the false-positive rate of the active fingerprint set against material with no corpus exposure?

    Measure — Matches per control-attributed document; detector precision at each confidence band.

  • SQLBL/ST 05

    Continuous

    Crawler and agent access patterns

    Which retrieval agents access the corpus, at what depth, and which documents are downloaded rather than read?

    Measure — Requests by agent class, document, and format.

Controls

A proportion of the published corpus carries no intentional fingerprint. These control documents are indistinguishable from fingerprinted documents in the public catalogue by design; their status is recorded internally so that background and false-positive rates can be measured without biasing the reader.

Ethics and scope

SQLBL studies documents, not people. Detector output describes evidence about a text and is not a determination about a candidate’s honesty, competence, or authorship. No SQLBL document should be used as a hiring screen.