A commentary analyzing the Government's fiscal policies, arguing that while spending has been redirected to essential services, overall expenditure remains above targeted caps and lacks true fiscal restraint.
How the framings classify across 3 articles. Each framing is labelled by a small AI stance classifier; see the methodology page for details.
Stacked weekly counts; colour by lean. “n/a” covers government and iwi-Māori sources where lean isn't applicable.
How this topic has been named, week by week. A new alias winning out is usually a framing shift.
Verbatim segments from politicians speaking on podcasts and radio shows about this topic. Sourced via the voice-reference library — each speaker has been confirmed manually from their voice clip. Click play to stream the original audio from the publisher, pre-seeked to the moment the quote starts.
Well, I I I've been experimenting with it in my office recently, and it's incredible. So instead of uh one of my analysts spending half a day coming up with a document, they said to AI, have a look at public service reform around the world, tell us who's done what, what seems to have worked well, what hasn't. Uh, and ten minutes later, you've got a beautiful document with some guidance and some advice. Now, normally that would take hours of human time. Then you put a human over the top and you think about it a bit more carefully. So it reduces mundane tasks to mere minutes.
Up to 12 framings spread across orientations. Each framing is a short phrase the topic extractor generated to characterise the piece's stance — not a quote from the source. Click through to read the original.
a structural safeguard
Nicola Willis: Finance Minister on the public service reform, experimenting with AI to streamline tasksSocial-media signal on the same topic, drawn from the social lens. Engagement is likes + 2×shares + 3×replies, the same weighting used across the digest cards. View on /social →
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