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What the picker changes
  • Top topics digest — the cards score the selected period against the prior 4 weeks.
  • 12-week heatmap & outlet matrix — show the 12 weeks ending at the selected week (they slide back with the picker, they aren’t a fixed snapshot).
  • Per-topic volume / alias drift — same 12-week trailing window, anchored on the selected period.
  • Coverage gap quadrant — scores the selected period against the 12 weeks before it (not including it).
  • Anomaly cards — only show alerts the detector fired during the selected period. Quiet weeks legitimately show none.
What stays as-is
  • Outlet orientation strip / lean colours — context-only, drawn from the last 12 weeks of activity regardless.
  • Co-occurrence graph — recent-activity anchored, not picker-driven.
  • Source & topic profiles — all-time data for the topic; the picker doesn’t affect them.
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Topic

Ai-Powered Dynamic Pricing

4 items · 3 aliases · peaked week of 26 Apr 2026 · first seen 28 Apr 2026

A commentary exploring the rise of AI-driven dynamic pricing in retail, particularly in clothing, and raising concerns about transparency, consumer manipulation, and regulatory oversight in New Zealand.

Volume by source orientation Methodology →

Stacked weekly counts; colour by lean. “n/a” covers government and iwi-Māori sources where lean isn't applicable.

Alias drift

How this topic has been named, week by week. A new alias winning out is usually a framing shift.

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In the press Methodology →

How the news corpus has covered this same topic over the last 12 weeks. 1 article from RNZ, Stuff, NZ Herald, ODT, 1News, Newsroom and The Spinoff. Click through to the press view for the full panel.

12-week press volume 1 article
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Sample framings

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.

spinoff Centre-left

ethical concerns about consumer manipulation

Is AI-powered ‘dynamic pricing’ coming for clothes shopping?
28 Apr
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How the public reacted

Social-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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