New MRI Studies Support the Blanchard Typology of Male-to-Female Transsexualism
Bibliographic record
Abstract
Twoindependentempiricalarticleshaverecentlyappearedintheliterature that, taken together , bear out an hypothesis Blanchard(2008) postulated in the Archives about brain development intranssexualism:[T]he brains of both homosex ual and heterosexual male-to-female transsexuals probably differ from the brains oftypicalheterosexualmen,butindifferentways.Inhomo-sexual male-to-female transsexuals, the difference doesinvolvesex-dimorphicstructures,andthenatureofthedif-ference is a shift in the female -typical direction. If there isany neuroanatomic intersexuality, it is in the homosexualgroup. In heterosexual male-to-female transsexuals, thedifferencemaynotinvolve sex-dimorphic structuresatall,andthenatureofthestructura ldifferenceisnotnecessarilyalong the male–female dimension. (p. 437)Blanchard’s prediction follows from studies that have repeat-edly shown that the homosexual male-to-female transsexualsare‘‘female-shifted’’in multiple, sexually dimorphic character-istics,whereastheheterosexual male-to-femaletranssexualsarenot(Blanchard,1989a,1989b).Forexample,homosexualmale-to-female transsexuals are sexually attracted to natal males,expressgreaterinterestinfemale-typicalactivities(eveninchild-hood), and are naturally effemina te in mannerism. In contrast,heterosexual male-to-female transsexuals are indistinguishablefrom nontranssexual natal males o n these variables. The hetero-sexual transsexuals are still dis tinct from typical males in otherways, however, such as by manif esting ‘‘autogynephilia’’—theeroticinterestinorsexualarousalinresponsetobeingorseemingfemale. The consistent detection of cross-sex features amonghomosexual male-to-female tra nssexuals, but not among heter-osexual male-to-female transse xuals, led Blanchard to predictthat the cross-sex pattern would also emerge at the level of brainanatomyandbelimitedtothehomosexualmale-to-femaletrans-sexuals.Thatpredictionnowappearstobethecase,withRamettiet al. (2010) supporting his prediction for the homosexual trans-sexuals, and Savic and Arver ( 2010), for the heterosexual trans-sexuals.The Rametti team used an MRI technique called DiffusionTensor Imaging to compare homosexual male-to-female trans-sexuals(n=18)withnontranssexual,heterosexualcontrolmales(n=19) and with nontranssexual, h eterosexual control females(n=19). They contrasted the male controls with the femalecontrols to identify the sex-dimorphic portions of the brain andthen contrasted the homosexual transsexuals with each of thecontrol groups on the dimorphic brain regions so identified. Theinitial contrasts identified six sex-dimorphic brain regions. Thehomosexual transsexual sample w as intermediate in volume onall six brain structures, significan tly different from the male con-trolsonfiveofthesix(andsignific antlydifferentfromthefemalecontrols on all six). That is, the se male-to-female transsexualsweredifferentfromthecontrolmales,shiftedtowardsthefemaledirection on all parameters.Savic and Arver (2010) applied anatomical MRIs with ananalogousresearchdesign,identifyingthesex-dimorphicpor-tions of the brain and contrasting the (this time) heterosexualtranssexual sample (n=24) with each control sample (n’s=24each)onthesex-dimorphicbrainregions.Oftheeightbrainregionsthatdistinguishedmalefromfemalebrains,thehetero-sexual transsexual sample differed from the male controls onnone(Savic&Arver,2010,Table3).Ofthefourbrainregionsthat distinguished these heterosexual transsexuals from themale controls, sex-dimorphism was present in none (Savic &Arver,2010,Table3).AsSavicandArverthemselvesempha-sized, ‘‘Contrary to the primary hypothesis, no sex-atypical
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".