Bibliographic record
Abstract
Tordoff et al.’s comparison of “two-step” approaches for measuring gender identity1 makes an important contribution, aiming to ascertain transgender status (gender modality) while respecting the safety, dignity, and diversity of gender minority individuals. This work is particularly timely considering unprecedented attacks on transgender and gender-diverse people’s rights by state and federal governments, including the weaponization of information about sex assigned at birth. Notably, the current political context heightens the perceived and actual risk to transgender people of answering survey questions about sex and gender. The authors’ analysis, which found “near perfect agreement” between the established two-step approach (sex assigned at birth + current gender identity) and a measure of transgender identity, drew on an LGBTQ+ (lesbian, gay, bisexual, transgender, queer, and other related identities) community-recruited sample. Although LGBTQ community surveys are vital sources of information on the health and social experiences of sexual and gender minority persons, they are not ideal for testing measures intended for use in population-wide surveys. As the authors acknowledge, cisgender LGBQ people are unrepresentative of the broader cisgender population. They are more likely than their straight peers to be familiar with concepts such as sex assigned at birth and transgender status, and this gap would be magnified among cisgender LGBQ individuals who self-select to participate in an LGBTQ+ community survey.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.105 | 0.201 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.022 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".