The pipeline’s output is not free text. Each institution’s findings are consolidated into a fixed, documented structure: a published schema that the model-mediated steps are held to and that the final writing step validates before anything is recorded. Output is organised as three related tables.
- Activities
- One row for each distinct generative-AI activity identified at an institution: what it is, what kind of tool it involves, how far adoption has progressed, and what governance measures were documented alongside it.
- Sources
- One row for each page that supports an activity, carrying the address, the title, the date the page was published where available, the date we retrieved it, and an assessment of how authoritative the source is. Some sources are recorded as supporting no particular activity, because they established that no evidence was present, or were too ambiguous to attribute.
- Institution summary
- One row per institution per run, recording the institution-level finding, counts of the activities and sources behind it, and the confidence and uncertainty flags carried forward from them.
Every row carries provenance: which run produced it, on what date, with which model and which piece of software, and an identifier that links a record to its supporting sources. This is the mechanism by which any record on this site can be traced back to the page it came from and checked. Where a value was supplied by a fallback rather than read from the page, that too is recorded, so an assumed value is distinguishable from an observed one.
Provenance also records which languages the search for that institution actually ran in. That field is derived from the configuration the run actually used rather than being set independently, specifically so it cannot overstate or misdescribe what was searched. A reader assessing whether an institution’s coverage is likely to be complete should read it alongside the record.
The structure records the institution-level finding and the activity-level detail separately, because they answer different questions and can be revised independently. Records are also scoped to the run that produced them: identity that persists across runs belongs to the institution registry, not to these tables.
Reading absence, and reading review state
One way to misread this dataset, and a consequential one, is to treat the absence of a record as a finding. It is not one.
An institution with no recorded activity may be one where any of the pathways in Where evidence can be missed intervened: the page was never surfaced, the wrong website was identified, a relevant address was filtered or a relevant page judged not worth reading, retrieval failed, extraction did not recognise what was there, consolidation rejected the record, or the run never reached the step that writes records. Or it may be an institution where no public evidence of generative-AI activity exists to be found.
Only the last of those is a statement about the institution. The others are statements about the pipeline’s processing. They produce the same visible result (no record) and they are not interchangeable. An institution that has not been examined is not thereby an institution found to be doing nothing, and no analysis should treat an empty result as a zero without first establishing which of those outcomes applies.
The same caution applies to review. Records differ in how far they have been through the pipeline’s own checking steps and any subsequent human review, and that status is recorded rather than implied: how far a run went travels in the record’s provenance, and whether anyone has looked is its evidence state, in the vocabulary Review states defines. Read a record together with its provenance and its evidence state; neither the presence of a record nor its absence is self-explanatory without them.
Validation
The two pages before this one describe how the pipeline works and where it can fail. They do not tell you how often it fails, and we are not in a position to tell you that yet.
The pipeline contains one internal checking step: the consolidation stage reconciles the claims extracted from several pages for a single institution, resolving disagreements between sources and carrying uncertainty forward. That is a consistency check on what extraction produced. It does not compare anything to an independently established answer, and it cannot detect an error that extraction and consolidation share.
Measuring the pipeline against independently established answers is separate work, currently in design. It will estimate, step by step, how often relevant evidence is missed and how often a recorded finding is not supported, by comparing pipeline output against a set of records coded by hand. A comparison against what a straightforward AI-assisted web search would return is planned alongside it. Until that work is complete and its results are approved, this page publishes no figure of any kind about the pipeline’s accuracy, consistency or error rates, and readers should treat any such figure attributed to G3O as unverified.
How to use this data
Records here are evidence-backed outputs of an automated process. They are not verified facts, and they are not a census. Each one asserts that a specific page, retrieved on a specific date, was read as describing a specific activity, and no more than that; the page it came from is recorded so the assertion can be checked.
- Check the provenance before relying on a record. The supporting source is recorded precisely so it can be read.
- Do not read an empty result as a negative finding. See Reading absence above.
- Weigh the language limitation before comparing countries, since discovery currently runs in English.
- Do not interpret a record beyond the evidence and the processing that produced it, including how far through the pipeline the run in question went.
- Keep three questions apart. What institutions exist is answered by the registry; what evidence was found is answered by this pipeline; what has been checked by a person is a separate matter again.
Validation evidence will be added to this page when it is complete and approved. Until then, the honest description of this dataset is that it is a systematic, documented, and inspectable attempt at broad coverage whose error characteristics have not yet been measured.