Is there any policy for a refund if someone drops out sufficiently early?
A link can be both affiliate and smile, they stack.
There's a plug in that will look for PDFs for you that match the page you're on or the text you have highlighted.
This sure seems like it should work. My experience is that there's either nothing, or whatever quality analyses exist are drowned out by pap reviews (it is possible I should tolerate reading more pap reviews in order to find the gems). However I think you're right that for issues that have an academic presence, google scholar will return good results.
My experience is that readability doesn't translate much to quality and might even be negatively correlated, because reality is messy and simplifications are easier to read. I do think works that make themselves easy to double check are probably higher quality on average, but haven't rigorously tested this.
I originally named the types of knowledge "Type 1", "Type 2", and "Type 3", but was encouraged by early reviewers to actually name them. In light of the conversation here, I think doing that was a mistake. Unless I was sure my names were out of the park correct (and maybe not even then), I should have left it generic so I could get input on more people for what the names, and for that matter definitions, should be.
A thing I really structured to capture was that "i did actual research and had actual models for why masks would help against covid, but it's still not type-3", which is why "know why" doesn't feel right to me.
I tentatively think that some of what you're calling engineering knowledge would fit into what I call scientific (which is a strike agains the names), and/or that I didn't do a good enough job explaining why engineering knowledge is useful.
Ignoring the labels I put on them, do you feel like you have a good sense of what I mean by each kind of knowledge? if so, what would you label them?
Welp, I did not make that deadline. Unfortunately the conditions that led me and the LW team to miss that deadline- high opportunity costs- are not likely to change soon, so instead of holding out for perfection I'm just going to share a couple of thoughts.
I was brought on to lead covid research efforts at LW as an experiment. The hope was that there was significant untapped research capacity, which could be unlocked by providing some structure (hence the research agenda). The structure was not only supposed to give people a sense of what would be useful to research, but reassurance that their research would actually be used, and social reinforcement. This mostly did not pan out- I think I did useful research during the time in question, I think other people produced useful research during that time, but questions I asked tended to be answered by only me.
The experiment was well worth running, and the team got a lot of information on infrastructure useful to support coordinated research (most notably it led to some reworks of Questions). But after 6 weeks it was not achieving its stated goal and had not found something clearly high value to pivot to, so I called it.
I think "prevent" is a strong word but broadly agree that my original approach was not conducive to deep model building. You can read more about how my thoughts have changed over time here.