I thought econ (especially among the social sciences) is famously quite a critical discipline where people are happy to tear into a colleague’s work.
I think one way the author probably avoided that kind of critical feedback is by not presenting this paper at conferences and in department work-in-progress sessions. Those are venues where work gets scrutinized and criticized and improved. But given that this was just outright fraud, he probably didn’t go through those community checks, because those would be opportunities to be exposed.
Just a few days after the preprint was released the work was presented at a NBER conference. Unfortunately, I think asking difficult questions during conferences is becoming more and more frowned upon. Especially if it is a man asking a tough question to a woman or a white person asking a tough question to someone from a minority group. It's a tough social climate for autistic and highly disagreeable people.
For what it's worth, it feels like ML Twitter is unusually adversarial, with people poking holes in viral papers. In contrast, computational biology Twitter feels less critical, with replies and QTs of papers tending to be congratulatory about the accomplishment, rather than engaging with the claims. It's obviously impossible to say if this has any causal relationship with the fact that ML seems to be progressing more rapidly than computational biology, but it is interesting.
I thought econ (especially among the social sciences) is famously quite a critical discipline where people are happy to tear into a colleague’s work.
I think one way the author probably avoided that kind of critical feedback is by not presenting this paper at conferences and in department work-in-progress sessions. Those are venues where work gets scrutinized and criticized and improved. But given that this was just outright fraud, he probably didn’t go through those community checks, because those would be opportunities to be exposed.
Just a few days after the preprint was released the work was presented at a NBER conference. Unfortunately, I think asking difficult questions during conferences is becoming more and more frowned upon. Especially if it is a man asking a tough question to a woman or a white person asking a tough question to someone from a minority group. It's a tough social climate for autistic and highly disagreeable people.
For what it's worth, it feels like ML Twitter is unusually adversarial, with people poking holes in viral papers. In contrast, computational biology Twitter feels less critical, with replies and QTs of papers tending to be congratulatory about the accomplishment, rather than engaging with the claims. It's obviously impossible to say if this has any causal relationship with the fact that ML seems to be progressing more rapidly than computational biology, but it is interesting.