Third—various cultural factors. You can't be trans if your culture doesn't have a concept of "being trans", and the concepts [and incentives](/2017/Dec/lesser-known-demand-curves/) that your culture offers, make a difference as to how you turn out. People who think of themselves as trans women in today's culture, could very well be "the same" as people who thought of themselves as drag queens or occasional cross-dressers 10 or 20 or 30 years ago. (Either "the same" in terms of underlying dispositions, or, in many cases, just literally the same people.)
-If there are multiple non-mutually-exclusive reasons why transitioning might seem like a good idea to someone, then the decision of whether to transition could take the form of a liability–threshold model: males transition if the _sum_ of their femininity, autogynephilia, and culture-related-trans-disposition exceed some threshold.
+If there are multiple non-mutually-exclusive reasons why transitioning might seem like a good idea to someone, then the decision of whether to transition could take the form of a liability–threshold model: males transition if the _sum_ of their levels of femininity, autogynephilia, and culture-related-trans-disposition exceed some threshold (given some sensible scheme for quantifying and adding (!) these traits).
-So 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 conjecturing that the distribution represented by this Bayesian network (if we had the complete network) can 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".
+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 conjecturing 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".
+
+[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]
[Berkson's paradox is also a thing]
[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 hhy 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_?
-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.
+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.
-[femininity->transition would be obvious even if it weren't in th]
+The femininity→transition arrow is obvious. The sexual orientation→femininity arrow (representing the fact that gay men are more feminine than straight men), besides being stereotypical folk knowledge, has also been extensively documented, for example by [Lippa](/papers/lippa-gender-related_traits_in_gays.pdf) and by [Bailey and Zucker](/papers/bailey-zucker-childhood_sex-typed_behavior_and_sexual_orientation.pdf).
-[Sexual orientation effect on femininity documented by Lippa and Bailey-Zucker]
+The v-structure between
[ETLE sexual orientation AGP v-structure, and effect of AGP on transition documented by Lawrence]
[I don't have a good formal citation on cultural factors, but it seems really obvious if you've been paying attention for the last decade]
+]
[quantifying the two-type effect:
Lippa 2000 "Gender-Related Traits in [...]"
that's d=–1.61 between gay and straight men
a gay man only needs to be 1 standard deviation (.48-.36 = 0.12) more feminine than average to be as feminine as a straight women
whereas a straight man needs to be (.68-.36 = 0.32) 0.32/0.12=2.67 more feminine than average to be as feminine as a straight woman—that's rarer, but not impossible
+
+In percentile terms, 1-norm.cdf(1) = 0.15 of gay men are as feminine as a woman
+whereas 1-norm.cdf(2.67) = 0.003 of straight men are
+that's a likelihood ratio of 50 ... but the prior is not that far from 50:1 in the other direction! They cancel out!!
+
+For concreteness: what does the Bayes net spit out if 3% of men are gay, and 5% are AGP, and whatever other assumptions I need to make this work?
+Suppose gays transition if they're 2-sigma feminine ...
+
]
[further implications: as cultural factors increase, the late-onset type becomes more of a "NOS" rather than AGP type]
projects / Ultimately_Untrue_Thought.git / blobdiff