You've made the mistake of thinking that if IQ were 50% genetic (it isn't - it's way more than that, but that's beside the point), then the remaining proportion is completely (non-shared) environmental.
Researchers in this field actually break down the non-genetic component into four major components:
1. Shared environment
2. Non-shared environment
3. Error
Shared environment accounts for so little variance that it might as well be ignored; while non-shared accounts for little more than error.
Note that including error in the non-genetic component, just as you've done in your post above, you are viscerally downplaying the otherwise undeniably predictive link from genes to IQ. In other words, whatever number you give is automatically deflated due to the way a psychometric is measured.
This has never been the source of debate. Back when I was going through grad school in intelligence, people didn't have to overthink how they presented the data. Intelligence was already a mature field, and we discussed the data openly. But in the past couple decades or so, a lot of people such as yourself popped up, attempting to craft irrelevant, statistically incorrect arguments against the results of certain well-established psychometrics that happen to not fit within whatever mental world your brand of politics ascribes.
If you really cared about the data, you'd be discussing the numbers. But your interest in this previously niche topic isn't in understanding reality; it's in justifying your worldview, which is why you deny the established data, immediately present a caveat stating that the data doesn't matter in the first place, appeal to emotions, and finish it all off by claiming those who disagree with you have been brainwashed. None of those four arguments have any merit in a genuine discussion on this topic.
How can research claim the genetic component of IQ is so high when there are so many environmental variables that have to be accounted for in order for the genetic component to even be able to manifest?
For example, if two identical twins are separated at birth. If one is raised in an educationally rich and nurturing environment and the other is raised in a horribly abusive and neglectful environment, then I am not sure the two would probably score the similarly on any given IQ test despite their genetic commonalities. Meanwhile, I imagine things like eye color, hair color, etc. which have a strong genetic component would remain consistent between the two.
My understanding is that’s way at the upper end and there are other reputable studies (which you can search for) that put it down around 50% in twin studies, which doesn’t necessarily mean you’re going to be smart just because your parents are. But that’s irrelevant. The question being asked was: if IQ is genetic, why do we need teaching? This is like asking why you’d need ever need gas if you had extra-large fuel tank installed in your car.
I guess these workbooks usually come in two different "shapes" - one, a guided workbook with a high-level goal that combines several concepts, and another would be a practice worksheet where we do a bunch of exercise of the same algorithm (say, long division, calculating summations, or matrix multiplication) over and over. For the "workbook" pattern, we first discuss with the LLM the final goal (e.g. a scratch program that can calculate a rocket's position using the rocket equation). Then we flesh out the steps towards the goal - is it reasonable to add the math for air resistance? air resistance that decreases with altitude? gravity turn? how do we integrate the velocity and position for each frame? How can we relate the integration by step size to the underlying integral, by showing that the result gets more precise (but slower to calculate) the smaller your delta-t is? Then, produce a Scratch code sample that implements the velocity and position calculation. Of course, there are things subtly wrong with the code sample (usually, if the math formulas are well-known, they're correct), which requires debugging - just another type of problem-solving.
The second shape, worksheets, is a lot more straightforward. Just define the type of problem you want to practice and have chatgpt make a bunch of problems. Then switch to one of the newer reasoning models and have it work the problems, and refine to get rid of any bogey problems (for example, for polynomial exercise, you could tell it to make sure the roots are integers)
The worksheets are more "hands off" - I run them through the algorithm once and check their work once or twice and then let them do the rest. The important thing is that the worksheets are connected to their high-level goal, and they understand that in order to solve the big, hairy problem that they're interested in, they need to build up certain specific skills.
Usually the worksheet goal is a pretty substantial conceptual stretch for my kids so they need to go through a series of fundamental worksheets. But the great thing about the LLM is, you can just tell it you're having a problem understanding some concept and to help build the scaffolding by listing all of the required skills to understand a concept, and picking the ones that needs improvement the most and practicing them.
My approach draws a little from "The MathAcademy Way" - https://www.justinmath.com/files/the-math-academy-way.pdf but instead of building fundamentals evenly in all topics before advancing (like expanding a sphere), we look only at the scaffolding required to support some higher-level goal - it's sort of like the masters/PhD process but guided through existing human knowledge: https://www.openculture.com/2017/06/the-illustrated-guide-to... . As a side note, I think it's really fun to include the history (mathematicians who contributed to the ideas) as well as the notation (using the greek letters, explaining why it's common to use them). When the kids notice the names like Pythagoras, Newton, and Euler reappearing frequently, and get a sense of the time scale these discoveries happened on, they treat the current state - and their ability to go learn thousands of years of math in months - with more reverence.
I'm Booking.com's Genius Level 3, so perhaps this only applies to frequent users, but I've been refunded in full for a booking after the date of the booking without even giving the reason for the refund request.
I've shifted 100% away from AirBnb and to Booking and am still able to book whole apartments/houses. It used to be AirBnb=houses/apts, Booking=hotels. Now Booking covers both, is usually cheaper (especially considering the cleaning fees AirBnb listings usually charge), and has good customer support (in my experience).
That's no longer true and hasn't been for a long time. While the name comes from that concept, the "hedge fund" is now just any fund marketed to accredited investors.
His point was that a tax on unrealized gains can one day be applied to less wealthy people.
I can explain it to you with a simple example:
Imagine, for instance, you're making 50k a year and have a brokerage account that has appreciated by 100k and that you're being taxed 20% on unrealized gains in year x. In year x+1, say your account falls back to where it was before. You paid 20k plus another 10k, and you will need to set aside another 10k. In essence, your income has been reduced to 10k, which could make daily life impossible for many people making 50k annually, especially those with families.
My point was that just because we impose a new tax on unfathomably wealthy people, it doesn't mean that we are inherently going to impose that tax on normal people. We can't be so scared of our own shadow that we are paralyzed into inaction. So scared of new taxes imposed on ourselves that we don't try to tax the uber wealthy. At the end of the day this is a democracy (obv with flaws, but still a democracy.) If politicians start taxing your 100k investment fund the way they are taxing a 100M investment fund, vote them out.
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