Belonging
Community bonds, rootedness, and living cultural practices and heritage.
What bias does this lens correct?
News emphasizes division, conflict, and isolation, reinforcing a sense of atomization. Robert Putnam described in Bowling Alone (2000) how social bonds are eroding. But the focus on polarization hides the communities that are holding together. This lens corrects that.
What does this lens find?
Articles about communities holding together or growing: intergenerational bonds, local networks, mutual care, and initiatives that strengthen connection. Not nostalgia, but living communities.
What you will and will not see here
This lens is being widened to include bridges between groups and reconciliation. Until then it mostly shows community, care and civic life.
You will see
- People building or using a connection: a community, care, a shared place, shared time.
- People taking part in civic life.
You will not see
- Stories whose news is harm to these things, such as violence or hostility, even when the topic is relationships.
- Private family or celebrity milestones on their own, such as a birth, a love story or a career move.
- Togetherness built against another group.
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.
Connections between generations, young and old together
Strength of social bonds and local networks
Mutual support and interdependence between people
Sense of home, connection to place and community
Finding meaning through contributing to something larger
Unhurried, attentive connection with others
What this lens actually picked
Three stories the Belonging 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.