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Record W4391144317 · doi:10.1097/tp.0000000000004854

“Real Women”: The Shared Responsibility to Use Gender-affirming Language

2024· letter· en· W4391144317 on OpenAlexaff
Heather Badenoch, Terrie Butler‐Foster, Patricia A. Gongal, Lori J. West

Bibliographic record

VenueTransplantation · 2024
Typeletter
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsCanadian Blood ServicesUniversity of AlbertaTranslational Research in Oncology
Fundersnot available
KeywordsTransgenderTranssexualHuman sexualityGender studiesPreferencePsychologyPopulationIdentity (music)Transgender womenMedicineSocial psychologyDevelopmental psychologySociologyFamily medicine

Abstract

fetched live from OpenAlex

Dear Editor: “Transplantation of the uterus in the male rat” by Yang et al1 is promising research with the potential to advance understanding of options to bear children for people assigned male at birth who choose to have children. However, we write to point out that the language is neither gender-affirming nor inclusive for transgender people. Gender (the intrinsic deeply felt experience of being a man, woman, both, or neither) and sexuality (to whom a person is attracted) are confused and conflated.2 This is evident by referring to the surgical procedure as “heterosexual” and “transsexual” transplantation. Consider also the following excerpt: “Traditionally, gender remodeling changes their appearance and make[s] them look more like their preferred gender. However, the function to be a true woman remained unsolved.” “Preferred” is a problematic term because it implies choice both for the transgender person and for people who affirm the person’s gender. Gender is not a choice or a preference. People are assigned (without choice) male or female at birth. For some, this initial label aligns with their self-perceptions and identity (termed cisgender). Many other people (1.6 million in the United States3) later realize that the gender they were assigned at birth is not who they truly are. People may realize they are transgender, nonbinary, or other gender identities. The following excerpt is also problematic: “These results may provide a reference for bilateral transsexual UTx in animals and genetically 46 XY individuals who wish to become real women through transsexual UTx.” The term “real women” ignores the diversity of womanhood, reducing womanhood to reproductive abilities. Women (transgender and cisgender) are real women without any qualifiers; the capacity to give birth does not define womanhood. How women decide to express their gender (through clothing, hairstyle, hormone therapy, surgical procedures) also does not define womanhood. Regardless of what steps a person takes to affirm their gender, a transgender woman is a woman and a transgender man is a man. A person does not need to take any steps to be a “true woman” or “real woman.” Language is powerful in shaping cultural and social attitudes. Health disparities among transgender people are striking. Affirming inclusive spaces and discourse can mitigate minority stress, which is a documented driver of health disparities in transgender people.4 In the physical sciences, nearly half of transgender scientists have considered leaving their workplace because of a hostile climate or discrimination.5 We must do better. Research about transgender people should be authored, or at least steered, with a community advisory committee made up of transgender and gender-diverse people. We encourage journals in the field of transplantation to work together with members of various systemically discriminated communities to establish and publish diversity, equity, and inclusion writing guidelines, and to work with authors to ensure all submissions meet the standard before publication. Given its commitment to diversity, equity, and inclusion, Transplantation would be well-positioned to lead such an effort.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.045
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0040.007
Scholarly communication0.0070.009
Open science0.0030.003
Research integrity0.0160.033
Insufficient payload (model declined to judge)0.0090.005

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.

Opus teacher head0.071
GPT teacher head0.386
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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