I notice that while a lot of the answer is formal and well-grounded, "stories have the minimum level of internal complexity to explain the complex phenomena we experience" is itself a story :)
Personally, I would say that any gear-level model will have gaps in the understanding, and trying to fill these gaps will require extra modeling which also has gaps, and so on forever. My guess is that part of our brain will constantly try to find the answers and fill the holes, like a small child asking "why x? ...and why y?". So if a more practical part of us wants to stop investigating, it plugs the holes with fuzzy stories which sound like understanding.
Obviously, this is also a story, so discount it accordingly...
I agree it would be very good, and possibly an economic no-brainer. My point is just that what is discussed in the post works for a political no-brainer, by which I mean something that no one would bother to oppose. To get what you want you need a real political campaign, or a large scale economic education campaign. Even then it's difficult, imo, unless your proposals fit one of the cases I mention above.
That said, of you are thinking of the US there is an easy proposal to be done for medicine, which is making medical school equivalent to a college degree and eliminating the requirement of having already done college before to enter (see https://slatestarcodex.com/2015/06/06/against-tulip-subsidies/, which notes it's done that way in Europe, I add it's the same for law school etc.). It's not an earth-shaking reform but it could work exactly for that reason.
The problem is, licensed people have made an investment and expect to repay it by reaping profits from the protected market. Some have borrowed money to get in and may have to file for personal bankruptcy. So they will oppose the reform by any means at their disposal, for which I don't blame them (even if it is obviously against the general interest).
Such a reform would be doable in the following cases (1) it compensates the losers in some way (2) it's so gradual that current licensed will mostly retire before it's fully implemented (3) it is decided by a political faction that has no interest in the votes of the licensed and no sympathy for their concerns, while the licensed have no "hard power" to block the reform (and this third will never be fulfilled for a blanket effort on all licenses: in practice you get a party punching down on the least powerful people in the opponent's coalition).
As you see, it's a whole other order of complication with respect to the case presented in the post...
It's not a coincidence that Hegel came up with the Zeitgeist idea exactly in 1800s Germany...
My overall take is that this is an useful starting point, and that structural factors are often underestimated, but the model is too simplified to actually make predictions with any confidence.
On effectiveness and public health studies: the thread quoted says multiple times "in the US". I would be curious to know if this kind of things are done more elsewhere or it's an implicit assumption that it could be done only in the US anyway (which could very well be true for what I know, drug profits are way higher in the US after all).
Does anybody know?
My feeling is that many of the people which did not benefit tend to "generalise from one example" and assume that's true for most kids.
Actually, I (despite being generally pro-schooling) would say something stronger than you: there is a minority of people who are actually harmed by school compared to a reasonable counterfactual (e.g. home-schooling for some). Plus, many kids can see easily where the system is failing them, less easily where it's working.
Thanks for the review!
Regarding the "countering racism" doubts, I can see how the results should disprove at least some racist worldviews.
I think that an interpretation of human history among racists is the following: the population splits in to clusters, these clusters diverge in different "races", eventually one emerges as "the best" and out-competes or replaces all others, before splitting again. Historically, this view was used to justify aggressive expansionism, opposition to intermarriage, and opposition to any policy that could slow this process by helping races which were seen as lesser.
I think what he wants to say is that this picture is not supported by the genetic data, which shows instead population clusters which split and merge and split again among different lines, on a fairly fast timescale and without one population replacing the other (except arguably for the Neanderthals, but even then not completely). In other words, there's no darwinian selection at the racial level, and there has almost never been.
According to my understanding (which comes from popularized sources, not I am not a doctor nor a biologist) antibody counts are not the main drivers of long-term immunity. Lasting immunity is given by memory T and B cells, which are able to quickly escalate the immune response in case of new infection, including producing new antibodies. So while high antibody count means you're well protected, a low count some months after the vaccine could mean that the protection has reduced, but in almost all cases you will be protected for a much longer time. Note that low antibody count immediately after the vaccine would be different, but I don't know if this even happens in people with an healthy immune system. Unfortunately there is no easy way to test how many memory T/B cells you have against a specific virus, without even going into how responsive they are.
So I think testing for antibodies before giving third doses would still result in giving the booster it to many more people than need it. Depending on how many doses you save, and on the costs of testing vs vaccinating, it may still be worth it. But it's probably more practical at this time to give the booster to the people we expect have developed less memory cells, in other words the immunocompromised and maybe elderly people. For the others, I would simply wait to have more data, and ship the extra doses to poor countries.
For info, you can find most of the exercises in python (done by someone else than Ng) here. They are still not that useful: I watched the course videos a couple of years ago and I stopped doing the exercises very quickly.
I agree with you on both the praise and the complaints about the course. Besides it being very dated, I think that the main problem was that Ng was neither clear nor consistent about the goal. The videos are mostly an non-formal introduction to a range of machine learning techniques plus some in-depth discussion of broadly useful concepts and of common pitfalls for self-trained ML users. I found it delivered very well on that. But the exercises are mostly very simple implementations, which would maybe fit a more formal course. Using an already implemented package to understand hands-on overfitting, regularization etc. would be much more fitting to the course (no pun intended). At the same time, Ng kept repeating stuff like "at the end of the course you will know more than most ML engineers" which was a very transparent lie, but gave the impression that the course wanted to impart a working knowledge of ML, which was definitely not the case.
I don't know how much this is a common problem with MOOCs. It seems easily fixable but the incentives might be against it happening (being unclear about the course, just as aiming for students with minimal background, can be useful in attracting more people). Like johnswentworth I had more luck with open course ware, with the caveat that sometimes very good courses build on other ones with are not available or have insufficient online material.
On this I agree with you. But the Darwin issue is a bit of a special case - the topic was politically/religiously charged, so it was important that a very respected figure was spearheading the idea. Wallace himself understood it, I think - he sent his research to Darwin instead of publishing it directly. But this is mostly independent of Darwin's scientific genius (only mostly, because he gained that status with his previous work on less controversial topics).
On the whole, I agree with jbash and Gerald below - "geniuses" in the sense of very smart scientists surely exist, and all else equal they speed up scientific advancement. But they are not that above ordinary smart-ish people. Lack of geniuses is rarely the main bottleneck, so an hypothetical science with less geniuses but more productive average-smarts researchers would probably advance faster if less glamorously.
You could make a parallel between geniuses in science and heroes in war: heroic soldiers are good to have, but in the end wars are won by the side with more resources and better strategies. This does not stop warring nations to make a big deal of heroic exploits, but it's done to improve morale mostly.