Conversation

I’d like to see distributions of age of citations in papers aggregated by fields. Ie if a paper in field X is published in 2022 with 10 refs, how many are 1y-5y old, 6-10y old,…, 50-60y old etc. Take such data an aggregate by field. Then break that out by influence.
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I’d guess stem fields have a barbell bias: lots of recent papers and a few old ones. I’d guess HSS papers have an exp decay pattern. I’d also guess influential papers in any field have anomalous distributions. I think you could distinguish stale vs flourishing fields this way
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I’m actually interested in general discourses, not academic work, but can’t think of a way to test this kind of hypothesis with them. Blog links and twitter quotes would be similar data. The bot picks up on old links for eg.
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interesting thought, although the number of pubs per year in a given field has to be considered, too. Be a nice companion to total # of cites for each cite in the target papers.