[copsarebastards]

> Yes, but so what? How do you link this argument back? 70 million is a lot of people in MORAL terms. What about scientifically?

What about scientifically? Scientifically, there's no concept of negligible or not negligible. On what scientific grounds did you decide 1% was negligible?

The negligibility of a percentage is only choosable based on your values and how much you value what exists in that percentage. My argument is that in most contexts, you probably care about 70 million people. If that's not the case, you may be a sociopath. But my guess is that you aren't a sociopath--you're just operating under some temporary delusion that because you've decided to say 1% of people instead of 70 million people, your decision that the group of people in question is negligible is scientific.

> We aren't talking about killing 70 million people. We're talking about the strength of constructs in terms of scientific utility.

If you're claiming that 70 million people have no scientific utility, I'd like to see what utility function you're using.

> This is also the problem with huge numbers. It's very hard to process and we are intuitively intimidated by the largeness, such as with numbers from the state budget. $70 billion? Oh my god. How am I supposed to process that number?

I'm not sure how the fact that large numbers are hard to process means that 70 million people is negligible. Certainly saying 1% instead of 70 million makes it easier to process, but playing to human mental limitations isn't a particularly good source of truth.

> Also note that 1% is a figure arising from the most inclusive definitions.

I'll happily make similar arguments about 7 million people instead of 70 million.

[threatofrain]

Actually, there is a way to decide if something is scientifically better. All you have to show is that your construct is competitive within the ecosystem of constructs. You can weakly improve upon an existing model by adding tons of domain-specific complications, which is what should've happened. But instead of saying that there are two predominant sexes, along with many abnormal and discrete bins, they propose a "continuum".

The researchers found that, in the most inclusive definitions, 1% of the population isn't sufficiently accounted for by traditional constructs.

But there's no new theory here. How do I predict complications based on what factors? What's the new model? The "spectrum"? A spectrum is a scale with escalating and deescalating values as you travel up and down, where jumps in the spectrum are connected to jumps in prediction. As for abnormal and discrete bins, well, the scientific community already has that. What's new to the table? A reformation of language so that we avoid the word "abnormal"? But where's the improved model?

Also note that you propose that there's no way to think about scientific or construct "betterness". Yes there is. You can measure by complexity, prediction, explanation, or generalizability. These are just a few ways. But you waved away scientific discussion, and instead choose only to use the moral lens, and bring up sociopathy.

Also, the reason I am talking about human limitations in processing large numbers is because I am accusing the opposition of abuse. I am not saying you should believe me because of X, I'm saying beware of opposition arguments because they are abusive to human minds.

And on the matter of using percentages to interpret numbers, I return to my example of state budgets, because that is a place where politicians often abuse psychology by stating what appears to be extravagant numbers. By extending your statements, I might say that not only is $70B a lot of money, but so is $7B. But then what if you told me that $70B is less than 1% of the state budget? What did you just do to that number?

Honestly, 10,000 people dying is a lot. Therefore, let's not talk about construct validity?

[copsarebastards]

> Actually, there is a way to decide if something is scientifically better.

Not in a general sense, there isn't. "Better" can only be scientifically defined in terms of a utility function, a goal. If you're trying to conduct, copper is better than rubber, if you're trying to insulate, rubber is better than copper. If you're trying to provide adequate healthcare and social protection to people, then a lower margin of error would be better.

> All you have to show is that your construct is competitive within the ecosystem of constructs.

Competitive based on what utility function?

> But there's no new theory here. How do I predict complications based on what factors? What's the new model? The "spectrum"? A spectrum is a scale with escalating and deescalating values as you travel up and down, where jumps in the spectrum are connected to jumps in prediction. As for abnormal and discrete bins, well, the scientific community already has that. What's new to the table? A reformation of language so that we avoid the word "abnormal"? But where's the improved model?

I think an admission that the current model is inadequate goes a long way towards motivating the discovery of better models.

> Also note that you propose that there's no way to think about scientific or construct "betterness". Yes there is. You can measure by complexity, prediction, explanation, or generalizability.

Okay, so you've named a bunch of utility functions. Now do you really want to apply those to this situation? How do we apply these to the question of whether 1% is a negligible margin of error. Let's optimize for those:

1. Lower complexity: "everyone is a man" seemed to work back in the day. 2. Higher complexity: let's subdivide male and female. There are certainly other genetic traits besides X and Y chromosomes that we could include in our definition of sex. (Hint: It's silly to optimize for higher complexity, but why? I propose that the answer is based on your values.)