Today I Learned

The flip side of this is something I've noticed in academia that I've started calling the "crackpot fallacy" where early on crackpots pushing a perspective end up biasing the entire field against that perspective to the point they end up very slow to engage with quality efforts in a similar direction.

So in cosmology you had a guy who dedicated himself to essentially defining a "new physics" back in the 80s around the concept of a mirror universe. It was pretty much total nonsense and he really had rewrite everything to get it to work, which is never a good sign.

But recently the head of theoretical physics at the Perimeter Institute and a fairly well respected cosmologist who shares the name of a thing with Hawking ended up making a ton of headway across several papers based on the idea of a CPT symmetric universe which explains a number of unanswered phenomena, avoided falsification with CERN searching for particle that never showed up which would have invalidated it, and has testable confirmatory predictions likely to be evaluated in the next few years.

And yet most physicists outside of a small network of theoretical cosmologists have no idea about it and if introduced to it evaluate it with great skepticism because it 'sounds' like something they've learned to associate with crackpots.

We see the same thing in ML right now, where the Google engineer who thought the LLM was sentient ended up making anthropomorphizing LLMs a career jeopardizing move. So we have transformers modeling fluid dynamics accurately with Sora video generation and no one bats an eye at the claim the transformer replicated something complex it wasn't explicitly trained on, but most balk at the idea that a LLM trained on anthropomorphic data is accurately modeling tangential aspects which feed into that data (in spite of an increasing number of replicated research efforts that show there's quite a lot more going on than meets the eye).

In pretty much every academic field I've looked at, this pattern emerges.

A single crackpot can seed landmines along the path they tread for legitimate researchers who come anywhere near that ground later on.

It's especially bad for fields where there's less room for testable predictions or experimental results, as those can somewhat mitigate inherent research biases.

So while it's probably quite annoying to deal with crackpots, academics would be wise to also be aware of the inherent bias they pick up via those engagements and better distinguish between identifying crackpots by methodology rather than topic - leaving a better chance to avoid dismissing a false negative when good methodology shows up in a topic previously represented only by crackpots.