Today we launched scitrust, a tool for comparing trust among researchers based on their citations. What if you could see the bias of different academic researchers? Where their rankings overlap and differ?
It's also a demo of the uptrust tech: a trust graph applied it to 176,234 researchers and 856,433 papers in 302 subfields.
https://science.uptrusthq.com/
Why might this matter to you? We think that in a lot of scientific fields there’s less consensus than it seems, and this basically starts to show where those gaps are the biggest. Citation count pretends to be a global ranking but ends up promoting citation-maxing, instead of good science. We think trust-maxing would be a move in the right direction. Eg: gaming 'trust' in a system like this means getting independently trusted people to vouch for your work. Repeat endorsements from the same person don't add more trust mass, so goodharting it looks a lot more like doing good science than citation-maxing does
What you can do:
Try switching the viewer on any ranking, or compare two researchers side by side to see where they line up and differ.
Differences from a personalized PageRank:
- Includes positive and negative evidence - we treat distrust and uncertainty as first-class evidence.
- Asymmetric propagation of distrust - trust flows transitively, but distrust doesn't. Your friend's friend is your friend, but your enemy's enemy isn't your friend.
- New rank requires new independent trusted sources, not more citations
Disclosures:
- Citations are an imperfect proxy for trust
- LLM classification is lossy
- This is a self-run showcase of the engine behind UpTrust (a product we're building)
If you're a researcher whose field this covers, we’d love to hear from you. How well does this represent your actual view? How much better (or worse) is it than citation-count list, or whatever else is available?
If you have a dataset you’re curious to run this over, also let us know!