I feel like this creates more misconceptions than it clears up. It's very dismissive of something that is really in the early phases of being studied.
The primary effect that reading this had on me was the change in state from [owning a cloak hadn't occurred to me] to [owning a cloak sounds awesome; i am unhappy that i hadn't thought of it on my own]
The answer to the question "what proportion of phenotypic variability is due to genetic variability?" always has the same answer: "it depends!" What population of environments are you doing this calculation over? A trait can go from close to 0% heritable to close to 100% heritable, depending on the range of environments in the sample. That's a definition problem. Further, what should we count as 'genetic'? Gene expression can depend on the environment of the parents, for example (DNA methylation, etc). That's an environmental inheritance. I just think there is an old way of talking about these things that needs to go away in light of current knowledge.
I agree with you that we still can learn a lot from these studies.
Adoption studies are biased toward the null of no parenting effect, because adoptive parents aren't randomly selected from the population of potential parents (they often are screened to be similar to biological parents).
Twin studies I think are particularly flawed when it comes to estimating heritability (a term that has an incoherent definition). Twins have a shared pre-natal environment. In some cases, they even share a placenta.
Plus, the whole gene vs. environment discussion is obsolete, in light of the findings of the past decade. Everything is gene-environment interaction.
If you are not going to do an actual data analysis, then I don't think there is much point of thinking about Bayes' rule. You could just reason as follows: "here are my prior beliefs. ooh, here is some new information. i will now adjust my believes, by trying to weigh the old and new data based on how reliable and generalizable i think the information is." If you want to call epistemology that involves attaching probabilities to beliefs, and updating those probabilities when new information is available, 'bayesian' that's fine. But, unless you have actual data, you are just subjectively weighing evidence as best you can (and not really using Bayes' rule).
The thing that can be a irritating is when people then act as if that kind of reasoning is what bayesian statisticians do, and not what frequentist statisticians do. In reality, both types of statisticians use Bayes' rule when it's appropriate. I don't think you will find any statisticians who do not consider themselves 'bayesian' who disagree with the law of total probability.
If you are actually going to analyze data and use bayesian methods, you would end up with a posterior distribution (not simply a single probability). If you simply report the probability of a belief (and not the entire posterior distribution), you're not really doing conventional bayesian analysis. So, in general, I find the conventional Less Wrong use of 'bayesian' a little odd.