My beef isn’t with Shalizi’s reasoning, which is correct. I disagree with his text connotationally. Calling something a “myth” because it isn’t a causal factor and you happen to study causal factors is misleading. Most people who use g don’t need it to be a genuine causal factor; a predictive factor is enough for most uses, as long as we can’t actually modify dendrite density in living humans or something like that.
If g is a causal factor then “A has higher g than B” adds additional information to the statement “A scored higher than B on such-and-such tests.” It might mean, for instance, that you could look in A’s brain and see different structure than in B’s brain; it might mean that we would expect A to be better at unrelated, previously untested skills.
If g is not a causal factor, then comments about g don’t add any new information; they just sort of summarize or restate. That difference is significant.
A predictive factor is enough for predictive uses, but not for a lot of policy uses, which rely on causality. From your comment, I assume you are not a lefty, and that you think we should be more confident than we are about using IQ to make decisions regarding race. I think that Shalizi’s reasoning is likely not irrelevant to making those decisions; it should probably make us more guarded in practice.
I don’t understand your last paragraph. Could you give an example? Is this relevant to the decision of whether intelligence tests should be used for choosing firemen? or is that a predictive use?
The kinds of implications I’m thinking about are that if IQ causes X, (and if IQ is heritable) then we should not seek to change X by social engineering means, because it won’t be possible. X could be the distribution of college admittees, firemen, criminals, etc.
Not all policy has to rely on causal factors, of course. And my thinking is a little blurry on these issues in general.
My beef isn’t with Shalizi’s reasoning, which is correct. I disagree with his text connotationally. Calling something a “myth” because it isn’t a causal factor and you happen to study causal factors is misleading. Most people who use g don’t need it to be a genuine causal factor; a predictive factor is enough for most uses, as long as we can’t actually modify dendrite density in living humans or something like that.
Ok, let’s talk connotations.
If g is a causal factor then “A has higher g than B” adds additional information to the statement “A scored higher than B on such-and-such tests.” It might mean, for instance, that you could look in A’s brain and see different structure than in B’s brain; it might mean that we would expect A to be better at unrelated, previously untested skills.
If g is not a causal factor, then comments about g don’t add any new information; they just sort of summarize or restate. That difference is significant.
A predictive factor is enough for predictive uses, but not for a lot of policy uses, which rely on causality. From your comment, I assume you are not a lefty, and that you think we should be more confident than we are about using IQ to make decisions regarding race. I think that Shalizi’s reasoning is likely not irrelevant to making those decisions; it should probably make us more guarded in practice.
I don’t understand your last paragraph. Could you give an example? Is this relevant to the decision of whether intelligence tests should be used for choosing firemen? or is that a predictive use?
The kinds of implications I’m thinking about are that if IQ causes X, (and if IQ is heritable) then we should not seek to change X by social engineering means, because it won’t be possible. X could be the distribution of college admittees, firemen, criminals, etc.
Not all policy has to rely on causal factors, of course. And my thinking is a little blurry on these issues in general.
Seconding Douglas_Knight’s question. I don’t understand why you say policy uses must rely on causal factors.