I wonder if you could assign a citation tree score to each first-level citation.
For example, I cite papers A,B,C,D. Paper A cites papers 1,2,3,4. Paper 1 cites a retracted paper, plus 3 good ones.
We could say "Paper 1" was 0.75, or 75% 'truthy'. "Paper A" would be 3x good + 1x 075% = 3.75/4 = 93.7% truthy, and so on.
Basically, the deeper in the tree that the retracted paper is, the less impact it propagates forth.
Maybe you could multiply each citation by it's impact factor at the top level paper.
At the top level, you'd see:
Paper A = 93.7% truthy, impact factor 100 -> 93.7 / 100 pts
Paper B = 100% truthy, IPF 10 -> 10/10 pts
Paper C = 3/4 pts
Paper D = 1/1 pts
Total = 107 / 115 pts = 93% truthy citation list
If a paper has an outsized impact factor, it gets weighted more heavily, since presumably the community has put more stock in it.
I wonder if you could assign a citation tree score to each first-level citation.
For example, I cite papers A,B,C,D. Paper A cites papers 1,2,3,4. Paper 1 cites a retracted paper, plus 3 good ones.
We could say "Paper 1" was 0.75, or 75% 'truthy'. "Paper A" would be 3x good + 1x 075% = 3.75/4 = 93.7% truthy, and so on.
Basically, the deeper in the tree that the retracted paper is, the less impact it propagates forth.
Maybe you could multiply each citation by it's impact factor at the top level paper.
At the top level, you'd see:
Paper A = 93.7% truthy, impact factor 100 -> 93.7 / 100 pts
Paper B = 100% truthy, IPF 10 -> 10/10 pts
Paper C = 3/4 pts
Paper D = 1/1 pts
Total = 107 / 115 pts = 93% truthy citation list
If a paper has an outsized impact factor, it gets weighted more heavily, since presumably the community has put more stock in it.