Bibliographic record
Abstract
Nearly everyone is familiar with the term gay or homosexual, and some have even heard the acronym LGBTQ (lesbian, gay, bisexual, transgender, and queer/questioning). It is the T and the Q that need a little more discussion. Transgender is an overarching term for anyone who has a gender identity that differs from their genitals. A more contemporary acronym is transgender or gender non-conforming (TGNC). A non-binary or gender queer person is considered part of TGNC due to the non-conformity to stereotypical female or male gender roles and expressions. TGNC, specifically the non-conforming section, includes a vast array of gender expressions. Some examples of various GNC types are as follows: agender, gender fluid, bigender, two-spirit, and gender queer/queer (Austin, & Goodman, 2016; Frohard-Dourlent, Dobson, Clark, Doull, & Saewyc, 2016). Of those who self-identified as transgender or gender non-conforming in the National Transgender Discrimination Survey, 36% reported a non-binary gender identity (Klein, & Golub, 2016). A large Canadian study of transgender youth-young adults (Veale, Saewyc, Frohard-Dourlent, Dobson, & Clark, 2015) found that 70% reported more than one gender identity, and Kuper, Nussbaum & Mustanski (2012) found gender queer to be the most prevalent identity in a study of transgender adults. The reason any of this matters is related to the cultural and social emphasis that we in America, and in many Western countries, place on sex and assumptions of gender. If our worldview inserts people into the dichotomous boxes of male or female, it not only marginalizes (and endangers) those who do not cleanly fit into that binary, but it also effectively erases them. People will either hide who they are by choosing M or F on some form, or they will choose to live freely and face the social, political, and physical consequences of their authenticity. This forced binary occurs nearly everywhere: when you fill out your health history at the office of your healthcare provider, when you are called back to an examination room as Mr. or Ms. so-and-so, when you document a sex on your passport or driver's license, and in places where the information is frankly irrelevant, such as entering an online sweepstakes. Accessing a facility or activity that does not coincide with ones’ perceived sex can be met with social opprobrium and/or violence—something gender non-conforming or gender non-binary people face daily. This is not speculation—80 incidents of LGBT-targeted hate-based harassment and/or violence were reported in the 7 days subsequent to the recent contentious American presidential election (Southern Poverty Law Center, November 18, 2016). The increasing number (perhaps only because we are now measuring it) of genderqueer people begs the question—Are we becoming postgender in America? Postgenderists posit that gender assignments limit human potential due to their arbitrary and unnecessary nature (Dvorsky, & Hughes, 2008). A risk in emphasizing the fluidity of gender expression is exclusion of the transgender community—persons who feel a deep need to live life in a body different from their birth sex. However, the move toward postgenderism may be hindered, if not terminated in America, due to the recent presidential election. A Trump/Pence presidency in America may have substantial implications for the civil rights and freedom of gender queer people to live authentically. The cultural return of America to the 1940s either erases or oppresses non-binary people. It is important to note that dissolution of LGBTQ rights is a hallmark of Pence's career. There is no place for transgender or gender queer people in this worldview. This erasure is not lost on gender queer people. LGBTQ suicide hotlines were overwhelmed with calls when the winner of the presidential election became clear (Mettler, November 10, 2016). This presidential election only serves to remind us of how tenuous LGBTQ civil rights and safety really are. The election communicated that half of America including our family and friends was willing to trade LGBTQ civil rights and safety, for something else they perceived Trump and Pence to be offering (e.g., a tax break, school vouchers, a large border wall). There is a new reality in America, in many contexts, because of this presidential election. Boundaries on speech and behavior have been decimated and need to be redemarcated and re-established. As a culture, we need to move beyond the ‘bathroom police’ of antitransgender legislation that harms everyone—gender non-conforming people and America. As healthcare providers, we have an ethical obligation to advocate for and to care for all individuals, families, and communities. As nurses, we have a moral obligation to make every effort to rebuild and support a more accepting, humane, and tolerant society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".