"You're not better at science than many many scientists out there." As an unbiased, outside observer who's been a huge science nerd almost since birth, yeah I

"You're not better at science than many many scientists out there." As an unbiased, outside observer who's been a huge science nerd almost since birth, yeah I kind of am - in at least some very important respects. While I have no particular skin in this game (it's not my bread and butter, so to speak), I'm generally quite good at seeing the big picture on things (the forest vs. the trees). Plus I've rubbed elbows with enough PhD-types in my life, both inside academia and out in industry, to know that they very often don't really have a good grasp of reality. They can be pretty freaking clueless, in other words. As a computer nerd myself, I have no fear of downloading large datasets and analyzing them - and finding them wanting, perhaps. Nor do I have any particular faith in various computer models which are being relied on so heavily these days in fields like climate science. "Garbage in, garbage out", as the old saying goes. As a referee, how much of that kind of thing do you actually do yourself? Download the data sets and analyze them? (This amounted to thousands of files in my particular case.) Review the algorithms and the supposed logic behind them? Maybe even run the modeling software itself to see what the results look like, perhaps as you tweak various settings? Or do you just take whatever is given to you in those papers at face value? There are quite a few people out there now who have actually gone through these analysis steps - spending hours, days, maybe even weeks or whatever doing so- and they often have't been particularly impressed with what they've found. "there is no misaligned incentive structure here." Yeah there is, in that apparently there are quite a few researchers and others out there who will quietly tell you that they themselves may have real doubts about an issue (whatever it is), but are afraid to speak up too loudly out of fear of being beaten down professionally because of it. And then there's the whole "gravy train" problem, where folks will just jump on whatever bandwagon they think will get them funding and get their papers published and so on. This even though they themselves may not necessarily particularly qualified to speak in that area. In that I’m including things like saying “These ugly furry critters may be in real trouble now!”, which may not get them much traction. But change that to say “These ugly furry critters may be in real trouble now because of climate change!”, and then they may get lots of traction. There’s also the issue where negative results and results which go too much against the grain may rarely get published - because those peer-reviewed journals that everyone if supposedly so enamored of are first and foremost a business, after all, and they need to move product, so they will pick and choose whichever papers they think will help them do that. They also don’t want to go around ticking off their best customers (especially the ones who control the subscriptions), if publishing said research papers might make those customers look bad.