Methodology

The outcome that matters

The aim is to assess vulnerability to losing a job because of AI or automation, or to an involuntary reduction in paid hours that leaves earnings insufficient to live on. Faster tasks and hours saved are intermediate evidence. They do not directly measure this outcome.

A supported scenario must account for local pay and living costs, involuntary job or hours losses, and opportunities to find other paid work. No livelihood-risk model or numerical category thresholds have yet been approved. Missing assessments remain explicit.

Catalogs first. Evidence next.

Research assessments have not been published. All valid catalog combinations return null with research_not_published. No forecast model or scoring formula has been approved.

What a future category means

2030 and 2035 are scenario horizons. Automation pressure is not the probability that an individual loses a job. The five fixed bands are minimal, low, moderate, high and very high. Missing has a separate grey color. Numerical thresholds require research approval.

Task exposure, adoption, employment pressure, demand and competition are separate dimensions. Missing evidence never becomes zero or low risk.

Local demo

Demo requires ALLOW_DEMO_DATA=true on the server. Its fixed fictional fixtures test colors and transitions; they are not career advice. Target risk always uses the same country, horizon and release.