Actually, the problem is pricing. If we could identify and correctly value new concepts, then we can dispense with citations and just use the correct sum of concept valuations. Perhaps a correctly designed futures market would not only solve getting the right PhD students the right jobs, but bring a lot of speculative capital into fundamental research?
That's a very economics-minded approach. Also, I'm not quite sure what the futures would be about. That a paper will... get N citations? get a job for the first author? Achieve N stars on GitHub? N likes on social media? Be patented and put in a product? Turn X USD in profit? Bet on retraction? Bet on acceptance? On awards? Or replicability?
The first question is what scientific research is actually for. Is it merely for profitable technological applications? The Greek or the humanistic or the enlightenment ideal wasn't just that. Fundamental research can be its own endeavor, simply to understand more clearly something. We don't only do astronomy for example in order to build some better contraption and understanding evolution wasn't only about producing better medicine. But it's much harder to quantify elegance or aesthetics of an idea and its impact.
And if you say that this should only be a small segment, and most of it should be tech-optimization, I can accept that, but currently science runs also on this kind of aesthetic idealist prestige. In the overall epistemic economy of society, science fills a certain role. It's distinct from "mere" engineering. The Ph in PhD stands for philosophy.
The question 'what is science actually for' can be sidestepped. Everyone can agree that it has value, albeit we disagree on the actual value...this is why you need a market. As to how things get priced in such a market, this is a subject for further research...To start, it just needs to tie to something measurable. Heck we've created memecoins with far less backing. Also, we've carved up the conceptual space on a very course grained level with patents, we just need a more immediate, and granular system for doing so...
yes but you can train relatively dumb AI on high volumes of historical data and presenting factors. This is typically just a 'diagnostic assist', but often significantly better than a human
I did work on a diagnostic assist tool developed by a large pharma which diagnosed between Asthma and COPD. GPs get this right 52% time, specialists just over 60 and AI came in over 80%...using 12 relatively mundane input variables. I believe there are a lot of these situations, but not clear pathway to FDA approval
