- Joined
- Sep 9, 2021
The National Academics of Science has released a 200 page manual on how to collect stats on LGBT people. Colin Wright brought this up and noted how there were very few actual scientists involved in this document; after skimming through the chapters, you will find more sociologists than developmental biologists. The book can be found in its entirety here.
The chapter on sexual orientation generally outlines how the rates of true homosexuals have not grown over the years, barring bisexuals which have seen a marked uptick. But throughout that chapter, there is no mention of the word 'homosexual'. It is only 'same gender attraction' based on 'gender identity' and not sexual orientation, which they find 'too clinical'.
Here an excerpt from Chapter 10, dealing with sex in general:
NAS going with the tumblr dictate:
The chapter on sexual orientation generally outlines how the rates of true homosexuals have not grown over the years, barring bisexuals which have seen a marked uptick. But throughout that chapter, there is no mention of the word 'homosexual'. It is only 'same gender attraction' based on 'gender identity' and not sexual orientation, which they find 'too clinical'.
Here an excerpt from Chapter 10, dealing with sex in general:
Already they are going with the 'sex assigned at birth' thing, and have moved entirely on from sex to gender, despite writing many chapters on how the two are not the same. Looking through the reference list, nearly all of the sources are either data sets from the Williams Institute, intersectional feminist theory (I am not making this up, see the list here) and virtually nothing grounded in actual science.Measures of sex can include self-reported items that reference a person’s sex as it was assigned on their original birth certificate or as it is currently represented on their legal documents. These classifications in government records are only rough categorical proxies for more detailed and often continuous measures of sex traits, including aspects of anatomy (such as internal organs or external genitalia), physiology (such as hormone milieu), or genetics (such as chromosomes). Studies of sex traits show that human variation is not fully captured by a male–female binary distinction (Montañez, 2017). However, until recently, these were the only designations offered on most U.S. identity documents, including passports and driver’s licenses (see Chapter 3).
Because of their ubiquity in many data systems, binary sex categories have often been used in general survey research and in administrative and health contexts to describe and explain differences that may have roots in biology, social norms, or some combination of the two.1 Direct measures of sex traits better represent specific biological mechanisms that can produce observed sex differences, but such measures are not commonly used, even in health research and clinical settings (see Chapter 3). We consider sex trait measures further in Chapter 7, in the context of promising approaches to enumerate intersex populations. In keeping with the panel’s recommendation to collect gender by default (see Chapter 2) that emphasizes the importance of measuring gender, we limit our review of sex measures to the role
a measure of sex assigned at birth can play as part of a broader strategy of improved gender measurement and the enumeration of both cisgender and transgender people.
This leads to bold claims such as this:As noted in Part I, the absence of construct validity in most measures of sex and gender also contributes to using inconsistent terminology to describe binary distinctions between females and males (sex terms) or men and women (gender terms), in both research reports and everyday speech. Many of the measures we review in this chapter use a combination of sex and gender terminology in their question wording and answer options that continues to conflate the two constructs. For example, the sex terms of female and male frequently appear as responses to questions about both sex assigned at birth and gender identity. The practice of using sex terms in gender identity questions makes it challenging to maintain consistent terminology in our discussion; it also raises concerns about construct validity for these items. Given our focus in this chapter on improving gender measurement to include transgender and cisgender people, when not discussing a specific measure that uses sex terms, we use the gender terms, men and women, especially when discussing the conceptual underpinnings of different measures and the interpretation of resulting data.
The National Academy of Sciences believes sex is an 'imperfect proxy'. Once again, these are not actual scientists writing this shit. There is a reference to Harvard's intersectional feminist GenderSci lab, but there's a very interesting point to be found regarding research on mice:Although sex assigned at birth is an imperfect proxy for anatomical, genetic, and physiological sex traits, it has utility in health contexts—including survey research, clinical trials, public health surveillance, and medical settings—for purposes ranging from clinical decision support to exploring the role of sex traits in health status and the etiology of disease. In addition, asking for the sex assigned to someone at birth, instead of just a person’s “sex,” avoids problems inherent in assuming that sex is an absolute and static representation of sex traits by grounding the question in the experience of having been labeled with a sex, rather than identifying with it. As such, a two-step measure that includes sex assigned at birth is being collected for the National Institutes of Health’s major precision medicine research initiative, the All of Us program,3 as well as on national and local case report forms for surveillance of conditions such as HIV/AIDS4 and COVID-19.5 A two-step measure has also been incorporated in clinical data systems such as the U.S. Department of Veterans Affairs’ electronic medical record (EMR)6 and is reflected in standards for EMR terminology codified by the U.S. Office of the National Coordinator for Health Information Technology.7
Look at that. It's difficult to ascertain the 'sex binary' in humans, but there's no question about it in mice. Who would've thought?Q: Does sex contextualism apply to my research question?
Any time you study a variable that you think might be related to sex (hormone levels, reproductive strategies, etcetera), you have an opportunity to employ sex contextualism. Sex contextualism can inform your experimental design as well as your interpretation of your results.
Q: How can I avoid essentializing binary sex in my hypothesis?
Consider why you think sex could be an explanatory variable for the effect you’re studying. Try to isolate what sex-related variables you imagine impacting your variable of interest. Be sure to consider and measure variation in those traits not only between sexes, but also within them.
Q: How should I talk about sex and sex-related variables?
The terms “male” and “female” are totally compatible with a sex contextualist approach. We recommend that you specify and justify how you use “male” and “female” in your study to avoid overly broad claims. For example, newborn male mice, gonadectomized male mice, or wild male rats might all have different reactions to the same treatment, so specifying the exact study population is vital for precisely communicating your result.
NAS going with the tumblr dictate:
There is much talk about survey options and which one is best, because those who might identify as trans may not SAY they are trans:The two-step measurement approach also was designed to reflect the broadest definition of the transgender population, which categorizes as “transgender” any person whose gender identity is different from their sex assigned at birth, regardless of whether they identify with the word “transgender.” This definition is often called “transgender experience” (e.g., Puckett et al., 2020) or “transgender history” (as in the Scottish census question, described below). Not everyone with transgender experience or history expressly identifies as “transgender”: they may identify simply as men or women, or they may describe their gender identity using terms outside of the man/woman binary, such as genderqueer, genderfluid, gender-nonconforming, nonbinary, agender, bigender, or Two-Spirit.
On the intersex chapter, they offer a stat for how many there are:the one-step approach also does not work well in some survey modes: Using a single item on an online general population survey—with or without providing respondents with a definition of transgender—results in a much higher estimate of people who identify as transgender than is found in other surveys with interviewer-assisted modes (Saperstein and Westbrook, 2021). The “Do you consider yourself transgender?” question also had low test-retest reliability relative to subsequent responses for the same individuals on both a two-step measure and another similar one-step transgender identity question (Saperstein and Westbrook, 2021). Together, these findings suggest a higher rate of “false positives” for this question format in an online self-completion context.
So despite being diagnosed by a doctor, they did not follow up to confirm that their DSD was confirmed by a doctor. The reference list is the most scientific regarding DSDs, but with everything else, it's intersectional, 'they hate me because I'm black' bullshit. This is the manual used to collect data, and they used the same shit you see in CRT books.A 2020 survey conducted by the Center for American Progress (2021) included an intersex status question that simplified the first GenIUSS question listed above: “Have you ever been diagnosed by a medical doctor with an intersex condition?” The initial sample of self-identified LGBT adults was selected from a national, probability panel of U.S. households held by AmeriSpeak and was supplemented with respondents from a nonprobability opt-in online panel of respondents. Of 1,528 participants, 4.9 percent answered that they had been diagnosed with an intersex condition. The nonresponse rate was 0.9 percent. This sample was somewhat more diverse than the previous studies with respect to both race and ethnicity (59% White, 12% Black, 18% Hispanic, and 4% Asian) and education (34% with a bachelor’s or higher degree). The result of the 4.9 percent figure is far higher than usual estimates of intersex prevalence, but it was not possible to determine whether this was due to the overrepresentation of LGBT respondents or misreporting because there was no follow-up question to assess specific intersex variations or the rate of false positives.