My first degree was a double major in psychology and the philosophy of science, and I love this stuff. The idea that science is some fixed system breaks down remarkably easy
One brilliant exercise - take a list of fields and classify them into science, pseudoscience, and not science
This is usually fairly easy!
Now, try and describe ~why~ things fall into the categories they do
MUCH harder
Many scientific disciplines were created by charlatans and used for purposes that we would now describe as pseudoscientific. In the 1800s, some pseudoscientific areas were more on the ball than "traditional" medicine
And large parts of what we now mark as the imprimatur of "science" - white coats, labs, PhDs - are more historical than they are an essential part of the process
Then we have the areas that use what we'd usually consider as the "scientific method" - sociology, anthropology - but lack the certainty that physics gives us. They don't fit the classical mold of "science", but they do still do things scientifically
Anyway, the second you actually start to try and define the rules that govern why some things are considered science and others are not, you realize that a lot of it is pretty arbitrary
When Dawkins says that "science" is not socially constructed, what he means is that there are objectively true facts and that these are immutable, but the reality is that our system for discovering those facts cannot ever be without some construction
Yes, he partially clarified this, but the important point is not about whether there is or isn't an objective reality, but whether our system for examining that reality is objective
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@VPrasadMDMPH@CT_Bergstrom My favorite part of this is that we can actually do a fairly basic empirical test of whether the idea that twitter royalty is required to be a FB fact-checker is true, or whether it's simply a correlation due to pandemic expertise by looking at pre-pandemic follower counts
@VPrasadMDMPH@CT_Bergstrom Of the people quoted for the healthfeedback piece, the median number of twitter followers was 4,514, with two people having well below 1,000 prior to COVID-19. The mean is skewed up to 35k by Topol
2/n Paper is here, it's a pretty simple ecological study comparing countries on their deaths/million from COVID-19 and Google mobility data nature.com/articles/s4159…
3/n The authors modelled the impact of time spent in "residential" areas as shown by Google against number of COVID-19 deaths in different areas, and in most cases found that there was no significant explanatory power for this model
It was always predictable that COVID-19 denialism would morph into anti-vaccine advocacy because it was never about public health, it was always about attacking government measures
The Great Barrington Declaration was sponsored by an organisation that promotes tobacco smoking, denies global warming, and lies about asbestos. There's a reason no serious public health scientists signed it!
If your entire philosophy is predicated on reactionary outrage over any government intervention it's pretty much a given that you'd move on to being anti-vaccine when safe+effective vaccines came out for COVID
In patients where a clinical cause was able to be identified, the vast majority of cases were clearly deaths caused by COVID-19, which means that this is likely a true undercount of the deaths
Worryingly, only 1 in 7 of the children who died of COVID-19 had been tested for it beforehand
Also, this was a beautiful thing to see while reading the study. "We made a mistake at the start so we fixed it but here's all the data so you can tell for yourself" is absolutely the right thing to do when reporting on your trial outcomes!
I suspect when I do a formal risk of bias score for the study it will come out looking fantastic simply from this one thing. Researchers who are entirely open about their methods are the ones who publish the best studies!