Humans have a myriad of visible characteristics: height, weight, skin color, eye color and shape, hair color and type, shapes of facial features, and so on.
All these characteristics vary continuously.
If you see that data as points filling a high-dimensional cube, there's not going to be an empty space there.
Some areas are going to be denser than the others, but there are no gaps there.
What you try to do with "race" is you're trying to cluster this data.
But there really is only one cluster. Might as well call rand() a million times to get a bunch of points in 0..1, and cluster that.
Oh, but you've see black people! And white people!
Well yeah, but it's all those people filling the spaces in between any two points that make it impossible to draw the line.
The only way to draw the line is to make a call on where to draw it — that is, to make an arbitrary choice. Without it, your clustering algorithms would fail.
Yes, there are high-density peaks on this data, especially if you look at any single characteristic.
Yes, you can separate the peaks. But deciding on where to put the the threshold is choice — a social construct — that can leaves a lot of points without a "race" label (which race is Irish - Mexican?) and/or change which peaks make the cutoff (are Armenians a race, or noise in the dataset?).
>Maybe if diseases are distributed differently across different races, it can make testing and treating them more cost effective
The scientists have two options:
A) Look at the original data which you used to assign the race label (skin color, hair type, etc), and see if there's any correlation of that data with diseases
B)Look at the data, cluster it using an arbitrary choice to be able to get more than one cluster, ignore a lot of people below the threshold, assign the labels, IGNORE THE RAW DATA, and then look for correlations between labels and diseases.
Do you believe that life begins at conception? Or at birth?
Or is it somewhere in between? Do you feel comfortable marking the line where life possibly begins? If you mark it before birth, aren't you just giving ammunition to anti-abortion advocates to take away freedom from women? Should we just say that life begins at birth, and shut down anyone who asks otherwise, because anything else is dangerous and possibly arbitrary and not 100% accurate in rare cases?
Right. A bunch of variables all exist whose values let us ultimately apply the label of alive or not alive. So how can one pick the right set of variables and weight them appropriately to determine if a fetus is alive? Isn't it kind of arbitrary at a certain point? As in, we know when something is definitely not alive (when it is gametes in separate humans), and we know when something is definitely alive (when a baby is born and it is crying), but anywhere between those two is just an arbitrary choice.
You on your own definition of life would apparently want to restrict the rights of women relative to what they have currently. This is a dangerous idea and should not be allowed.
Yes, it is kind of arbitrary at certain point, that's why we have the debate about it in this country. I am not sure where you are going with this given the subject of the comment I was responding to.
I chose my definition from the rights-of-women perspective. My understanding is that a fetus is generally non-viable outside of a host body, and thus not "alive" by my definition.
On the other hand, we havd C-sections and incubators. If a baby can be safely extracted with a C-section, placed in an incubator, and survive, my understanding is that the choice to abort is no longer available.
Of course, I can be wrong here.
My point was that at least I can make a definition here that does not depend on a choice of an arbitrary value of a continuous variable. My definition of "alive" depends on the choice of which variables to look at, not on arbitrary thresholds. And I don't insist on it being The Truth.
Another example:
"Heart rate" is a continuous vafiable, but the distribution of its values has a large gap between 0 (no heart rate) and nonzero values (the lowest observed was 27bpm). So you don't need to make a choice for a clustering algorithm to work. You can run K-means on "heart rate" and get these 2 clusters: 0 and everything else.
This allows one to make a definition of "alive" based on heart rate. That's not my definition, but it's a usable one.
This is not feasible with race if we use the variables commonly understood to be associated with race: skin color, height, nose shape, etc. There are no gaps in those variables.
Even eye color varies continuously [2], and it's not clear how to assign labels [1].
Color in general is a good analogy for race. You see the colors vary in a rainbow. You can tell the difference between red, green, and blue.
But you can't run K-means on a rainbow to get 7 colors out. Or any number but 1, for that matter.
You need to make a call yourself where to draw those lines.
This reflects in languages. Russian has distinct words for what we'd call "blue" in English; But also English has words like cyan, turquoise, navy, etc., which other languages may not have.
Humans have a myriad of visible characteristics: height, weight, skin color, eye color and shape, hair color and type, shapes of facial features, and so on.
All these characteristics vary continuously.
If you see that data as points filling a high-dimensional cube, there's not going to be an empty space there.
Some areas are going to be denser than the others, but there are no gaps there.
What you try to do with "race" is you're trying to cluster this data.
But there really is only one cluster. Might as well call rand() a million times to get a bunch of points in 0..1, and cluster that.
Oh, but you've see black people! And white people!
Well yeah, but it's all those people filling the spaces in between any two points that make it impossible to draw the line.
The only way to draw the line is to make a call on where to draw it — that is, to make an arbitrary choice. Without it, your clustering algorithms would fail.
Yes, there are high-density peaks on this data, especially if you look at any single characteristic.
Yes, you can separate the peaks. But deciding on where to put the the threshold is choice — a social construct — that can leaves a lot of points without a "race" label (which race is Irish - Mexican?) and/or change which peaks make the cutoff (are Armenians a race, or noise in the dataset?).
>Maybe if diseases are distributed differently across different races, it can make testing and treating them more cost effective
The scientists have two options:
A) Look at the original data which you used to assign the race label (skin color, hair type, etc), and see if there's any correlation of that data with diseases
B)Look at the data, cluster it using an arbitrary choice to be able to get more than one cluster, ignore a lot of people below the threshold, assign the labels, IGNORE THE RAW DATA, and then look for correlations between labels and diseases.
Which approach do you think is more scientific?