I think that for anything except scholarship, those aren’t terrible. I’d attack them from the other side: They aren’t shallow enough. In industry, most often you often just want to find some specific piece of information, so reading the whole 30 pages is a waste of time, as is following your deep curiosity down into rabbit holes.
I agree with you. It’s a good point that I should have clarified this is for a specific use case—rapidly scouting out a field that you’re unfamiliar with. When I take this approach, I also do not read entire papers. I just read enough to get the gist and find the next most interesting link.
So for example, I am preparing for a PhD, where I’ll probably focus on aging research. I need to understand what’s going on broadly in the field. Obviously I can’t read everything, and as I have no specific project, there are no particular known-in-advance bits of information I need to extract.
I don’t yet have a perfect account for what exactly you “learn” from this—at the speed I read, I don’t remember more than a tiny fraction of the details. My best explanation is that each paper you skim gives you context for understanding the next one. As you go through this process, you come away with some takeaway highlights and things to look at next.
So for example, the last time I went through the literature on senescence, I got into the antagonistic pleiotropy literature. Most of it is way too deep for me at this point, but I took away the basic insights and epistemic: models consistently show that aging is the only stable equilibrium outcome of evolution, that it’s fueled by genes that confer a reproductive advantage early in life but a disadvantage later in life, and that the late-life disadvantages should not be presumed to be intrinsically beneficial—they are the downside side of a tradeoff, and evolution often mitigates them, but generally cannot completely eliminate them.
I also came to understand that this is 70 years of development of mathematical and data-backed models, which consistently show the same thing.
Relevant for my research is that anti-aging therapeutics aren’t necessarily going to be “fighting against evolution.” They are complementing what nature is already trying to do: mitigate the genetic downsides in old age of adaptations for youthful vigor.
I think that for anything except scholarship, those aren’t terrible. I’d attack them from the other side: They aren’t shallow enough. In industry, most often you often just want to find some specific piece of information, so reading the whole 30 pages is a waste of time, as is following your deep curiosity down into rabbit holes.
I agree with you. It’s a good point that I should have clarified this is for a specific use case—rapidly scouting out a field that you’re unfamiliar with. When I take this approach, I also do not read entire papers. I just read enough to get the gist and find the next most interesting link.
So for example, I am preparing for a PhD, where I’ll probably focus on aging research. I need to understand what’s going on broadly in the field. Obviously I can’t read everything, and as I have no specific project, there are no particular known-in-advance bits of information I need to extract.
I don’t yet have a perfect account for what exactly you “learn” from this—at the speed I read, I don’t remember more than a tiny fraction of the details. My best explanation is that each paper you skim gives you context for understanding the next one. As you go through this process, you come away with some takeaway highlights and things to look at next.
So for example, the last time I went through the literature on senescence, I got into the antagonistic pleiotropy literature. Most of it is way too deep for me at this point, but I took away the basic insights and epistemic: models consistently show that aging is the only stable equilibrium outcome of evolution, that it’s fueled by genes that confer a reproductive advantage early in life but a disadvantage later in life, and that the late-life disadvantages should not be presumed to be intrinsically beneficial—they are the downside side of a tradeoff, and evolution often mitigates them, but generally cannot completely eliminate them.
I also came to understand that this is 70 years of development of mathematical and data-backed models, which consistently show the same thing.
Relevant for my research is that anti-aging therapeutics aren’t necessarily going to be “fighting against evolution.” They are complementing what nature is already trying to do: mitigate the genetic downsides in old age of adaptations for youthful vigor.