Solutions
Technology, policy and initiatives already in use against a real problem, with results to show.
What bias does this lens correct?
When nothing seems to work, why bother trying? That's learned helplessness, a concept from Seligman (1972). When news constantly shows problems without solutions, people learn that action is pointless. This lens finds evidence that technology, policy, and initiatives do work.
What does this lens find?
Articles about technology, policy, or initiatives that demonstrably work: renewable energy at scale, successful policy, innovations reaching the market. Not promises, but proven solutions.
What you will and will not see here
You will see
- Something actually being done about a problem that harms people, nature, the climate, justice, democracy or peace, by someone you can name, with something to show for it.
- A technology in use, a reform in force with its effect shown, or a community practice that runs. It can be early, small or local.
- A new clinic, school, park or other health, education or community facility that is open.
- Roads, power lines and other infrastructure, but only when the article shows who benefits.
- A commitment that is binding and paid for: signed or enacted by those who will deliver it, with money set aside and a date.
- A court ruling that orders redress for named victims.
You will not see
- A problem with no response, or only a call for action.
- Pledges without money, forecasts, or previews of events that have not happened yet.
- New laws, rules, approvals or fines whose effect is not yet shown.
- A company praising its own product, funding rounds, or product reviews.
- A research finding nobody is using yet.
- Election news.
- An institution's own goal, such as efficiency or revenue, when no harm to people, nature, the climate or justice is being addressed.
An article can fit more than one lens. We publish it in the narrowest lens it passes. The lenses look for evidence that something happened. How well a story is told plays no part, except for a small share of the Discovery score.
Scoring dimensions
Each lens evaluates articles on six dimensions. Together they form the profile you see in the radar chart.
Is this a concrete, resourced action with measurable outputs, or aspirational rhetoric?
The actual or credibly-planned scale of the solution's reach
Is the claim backed by data, cases, or peer review — not just stakeholder quotes?
Strength of any policy reform, institutional design, or regulatory innovation
Strength of grassroots, mutual-aid, or local community practice
Does the solution distribute benefits equitably and lower access barriers?
Is the solution financially and resource-sustainable?
What this lens actually picked
Three stories the Solutions lens selected, spanning its range. The last one barely cleared the bar — it is here on purpose. A method is only judgeable if you can see the cases it nearly rejected. Headlines and summaries are written by AI; the scores are what the lens produced.
Notice that a story can pass on one strong dimension while scoring poorly on another. The lens does not require a story to be good in every way — it requires enough of what this particular lens is looking for. That is also how it gets things wrong, and why every story links back to the publication that reported it.
How does scoring work?
Our AI analysis system evaluates each article on the dimensions above with a score from 0 to 10. The weighted average determines whether an article passes the lens. Articles below the threshold are not shown. Not because they are bad, but because they do not fit strongly enough what this lens looks for.
Limitations
These dimensions are designed criteria informed by existing research. They are not established psychometric scales. The AI model can make mistakes: missing relevant articles or letting irrelevant ones through. The scores are a selection tool, not a definitive judgment of a story's value.
Want to know more?
Read how we work for the full picture, or browse the source code on GitHub.