You might ask: okay, but then where do the two types come from? This graph is just illustrating (conjectured) cause-and-effect relationships, but if we were actually to flesh it out as a complete Bayesian network, there would be additional data that quantitatively specifies what (probability distribution over) values each node takes conditional on the values of its parents. When I claim that Blanchard–Bailey–Lawrence's two-type taxonomy is a useful approximation for this causal model, I'm claiming that the distribution represented by this Bayesian network (if we had the complete network) could also be approximated a two-cluster model: _most_ people high in the "femininity" factor will be low in the "autogynephilia" factor and _vice versa_, such that you can buy decent predictive accuracy by casually speaking as if there were two discrete "types".
-Why? The key has to do with the parents of femininity and autogynephilia in the graph.
+Why? It has to do with the parents of femininity and autogynephilia in the graph. Suppose that gay men are more feminine than straight men, and autogynephilia is the result of being straight plus having an "erotic target location error", in which men who are attracted to something (in this case, women), are also attracted to the idea of _being_ that thing.
-Gay men are more feminine than straight men.
+Then the value of the sexual-orientation node is pushing the values of its children in _opposite_ directions: gay males are more feminine and less autogynephilic, and straight males are less feminine and more autogynephilic, leading to two broadly different etiological trajectories by which transition might seem like a good idea to someone, even while it's _not_ that the two types have nothing in common.
-And
+For example, among autogynephilic males, those who transition are going to be selected for higher levels of femininity, and in that aspect, their stories are going to have something in common with their androphilic sisters
-(Of course, it's _also_ the case that the component factors in a liability-threshold model would negatively correlate
+(Of course, it's also the case that the component factors in a liability-threshold model would negatively correlate among the population past a threshold.
the factors of a
-, due to Berkson's paradox,
+, due to Berkson's paradox.
+
+But I'm claiming the
[TODO—
-[The sexual orientation node increases femininity and decreases AGP, so those pathways are anti-correlated; however, the fact that straight AGP men also vary somewhat in their degree of femininity; some informal accounts (link Sailer) have emphasized how masculine (even hypermasculine) AGPs are, but this seems wrong]
-[briefly mention ETLE]
+the fact that straight AGP men also vary somewhat in their degree of femininity; some informal accounts (link Sailer) have emphasized how masculine (even hypermasculine) AGPs are, but this seems wrong]
[People who don't quite seem to fit the coarse taxonomy might still be explained by the graph and a threshold model]
]
-
You might ask: okay, but why do I believe this? Anyone can name some variables and sketch a directed graph between them. Why should you believe this particular graph is _true_?
Ultimately, the reader cannot abdicate responsibility to think it through and decide for herself ... but it seems to _me_ that all six arrows in the graph are things that we separately have a pretty large weight of evidence for, either in published scientific studies, or just informally looking at the world.
The v-structure between sexual orientation, erotic target location erroneousness, and autogynephilia has been documented by Anne Lawrence:
-
-
The autogynehilia→transition arrow has
The cultural-factors→transition arrow is obvious if you haven't been living under a rock for the last decade.
projects / Ultimately_Untrue_Thought.git / commitdiff