Your earlier statement, in which you claim that “money doesn’t effect result” followed by a useless distinction of high or low info elections. You’re really trying to dance a fine line of nonsense here.

“ We find a positive and statistically significant relationship between campaign expenditure, campaign contributions and winning probability.”

From the same article you posted and the first academic journal result if you Google “studies on how money influences elections”.

Maybe you've only spent time around PhD candidates at Ivy League schools where people are more likely to have access to wealth, but if you've spent any time at all around PhD candidates you'll find this is generally not the case. As a PhD candidate from a lower SE class, I've found that the majority of my peers are from a similar class. However, I am not connected to a private university though I am in medicine.

Studies on this show great variation across doctoral fields. Economics doctoral students tend to come from more affluent backgrounds, while the majority of Social Sciences doctoral students are from a lower SE class. Overall there seems to be a trend that doctoral students in fields with more lucrative career prospects tend to come from a higher SE class.

From what I have read, your claim only applies to the majority of faculty members, which tend to come from backgrounds with an income that is higher than the national median income.

>Initially during the prodrome, a change in brain structure seems to be present in the temporal lobe volume and cingulated. On follow-up in those who have gone onto a psychotic episode, further changes can be seen in the cingulate, temporal lobe, and parahippocampal gyrus.

Structural changes occur before an episode has even occurred.

It is true that Diet Coke has a higher caffeine content. Though I would not say it is significant:

A 12 oz. can of Coke contains 32 mg, Diet Coke contains 42 mg

A difference of 10 mg is not significant in this context.

That said, their view that it is simply a matter of collaboration and coordination is entirely wrong. Sharing of data and collaboration would absolutely be worthwhile (though it runs opposite to the direction of incentives in profit-driven drug development) but it's like saying we could start building a Dyson Sphere tomorrow and solve the worlds energy problems if we just pooled our talent and resources. In contrast to what the author claims, we need HUGE advances in technology and our understanding of the human body, pharmaceutical sciences, drug development, etc. before this is possible. To use their example of GLP-1 agonists, prior to their development and wide-spread usage, the psychological effects of these drugs were completely unknown. Both positive and negative, clinically. But what if those effects were much more dangerous? Many SSRIs have a black box warning, which is mostly applicable to specific age groups. Negative side effects that we see in teenage patients are much less common in other age groups. These kinds of effects are why medicine moves very slowly and experimental work is costly, because ultimately we are talking about peoples lives and not a machine that is easily replaced if we break it during the testing phase.

Millions of animals would be the first to rejoice and praise a model that didn't require in-vivo testing but, we may sadly never see that day. I'm skeptical that even the development of an ASI would be enough to get us there.

I did find that the author has a knack for explaining difficult concepts with simple and illustrative metaphors. As a clinician and scientist in the pharma research space, this is one of the few articles I would send to a friend that finds the topic interesting but lacks the background knowledge to understand most literature about drug research.