This is very slow going, because CaCiRI doesn’t seem to have a thesis. At least, I haven’t found one, and I’ve read almost half of the content. It’s just a bunch of facts. Often not even syntheses
frequent long quotes are a bad sign about a book.
I could say similar (if more positive) things about mass link posts like this one from SSC—the only way to compress the information they contain, is to send the link. (Although a book’s big enough maybe the information could be sorted by seeming importance, or relevance to other topics.)
I want to label this sin “weed based publishing” (as in, “lost in the weeds”, although the fact that I have to explain that is a terrible sign for it as a name).
Sounds like a newspaper.
Possible name: Has no case?
I want to label this sin “making me make your case for you”.
The obvious 2x2 would be “is there evidence” and “is there a claim”—but evidence + claim isn’t a sin, and no evidence + no claim is a different kind of book (if it’s a book at all).
Sin list:
1. Unconnected Evidence, without any claims.
It seems that “Unconnected” is correlated with ‘not having claims’.
2. (Connected) Claims, without Evidence.
A list of the important traits could use some fleshing out.
a new emphasis on extracting and evaluating the author’s models
“what we know” is inextricable from “how we know it”.
Here it is.
This is a pretty strong reversal for me. I remember strongly wanting to just be told what we knew in my science classes in college, not the experiments that revealed it. I’m now pretty sure that’s scientism, not science.
Incentive-wise, this might have to do with how the knowledge is tested.
If you know the conclusion, you can test it. Knowledge → application is a natural place to focus, though the best way to do that might involve some degree of experimentation (if in ways more profit concerned than science should be).
“what we know” is inextricable from “how we know it”.
If we know something, but we don’t know how we know it, then how can it be verified/disproven?
If don’t know something, but we know how to know it (the color of the sky is found by looking at the sky), then that can be fixed (look at the sky). (Although that starts to get into “what is the sky”—the way you define it effects answers to questions like “what color is it”.)
E.g. say there is a test T for cancer C, for which n% of positives track to real cancer, and the cancer has a d% 5-year risk of death. However the test preferentially picks up on deadlier forms of the cancer, so given a positive result, your risk of death is higher than n*d/100.
If I say “you have cancer C”, you’ll assume you have a 5 year risk of death of d.
If I say “You have an n% chance of having cancer C”, you’ll assume you have an n*d/100 5 year risk of death.
If I say “You tested positive on test T”, you can discover your actual chance of death over 5 years. So knowing the test result rather than the summary, even the detailed summary, is more informative.
OTOH, your estimates in scenario 3 will be heavily dependent on who gets tested. If the governing body changes the testing recommendations, your chance of death given a positive result on T will change. So knowing “you have n% chance of cancer” is in some ways a more robust result.
I could say similar (if more positive) things about mass link posts like this one from SSC—the only way to compress the information they contain, is to send the link. (Although a book’s big enough maybe the information could be sorted by seeming importance, or relevance to other topics.)
Sounds like a newspaper.
Possible name: Has no case?
The obvious 2x2 would be “is there evidence” and “is there a claim”—but evidence + claim isn’t a sin, and no evidence + no claim is a different kind of book (if it’s a book at all).
Sin list:
1. Unconnected Evidence, without any claims.
It seems that “Unconnected” is correlated with ‘not having claims’.
2. (Connected) Claims, without Evidence.
A list of the important traits could use some fleshing out.
Here it is.
Incentive-wise, this might have to do with how the knowledge is tested.
If you know the conclusion, you can test it. Knowledge → application is a natural place to focus, though the best way to do that might involve some degree of experimentation (if in ways more profit concerned than science should be).
If we know something, but we don’t know how we know it, then how can it be verified/disproven?
If don’t know something, but we know how to know it (the color of the sky is found by looking at the sky), then that can be fixed (look at the sky). (Although that starts to get into “what is the sky”—the way you define it effects answers to questions like “what color is it”.)
This feels like some of what I was getting at.
E.g. say there is a test T for cancer C, for which n% of positives track to real cancer, and the cancer has a d% 5-year risk of death. However the test preferentially picks up on deadlier forms of the cancer, so given a positive result, your risk of death is higher than n*d/100.
If I say “you have cancer C”, you’ll assume you have a 5 year risk of death of d.
If I say “You have an n% chance of having cancer C”, you’ll assume you have an n*d/100 5 year risk of death.
If I say “You tested positive on test T”, you can discover your actual chance of death over 5 years. So knowing the test result rather than the summary, even the detailed summary, is more informative.
OTOH, your estimates in scenario 3 will be heavily dependent on who gets tested. If the governing body changes the testing recommendations, your chance of death given a positive result on T will change. So knowing “you have n% chance of cancer” is in some ways a more robust result.