Integrating Gender Identity and Sexual Orientation in Population-Based Administrative Data
Bibliographic record
Abstract
Information regarding biological sex is collected for administrative and clinical purposes, and often is conflated with gender in clinical encounters as well as in population-based data studies. Sex is collected as either ‘male’ or ‘female’, adhering to the colonial notion of binary sex and gender identities. Meaningful information on sexual/romantic orientation is also absent from these datasets. Sex, gender, and sexual/romantic (SR) orientation are key aspects informing how people experience the world and how we interpret administrative data. SOGI-HE (Sexual Orientation and Gender Identity – Health Equity) will be the first 2SLGBTQIA+ dataset at the Manitoba Centre for Health Policy by using a novel survey tool to collect information on gender identity, and SR orientation. The data from this survey will be linked into the Population Health Data Repository housed at the Manitoba Centre for Health Policy, enabling analyses that incorporate constructs of sex, gender, and SR orientation. These data will make visible the experiences of the 2SLGBTQIA+ community that have been historically absent from population-based data studies. Data equity and 2SLGBTQIA+ visibility within data play an important role in advancing the field and lays the groundwork for future data linkage projects. In this presentation we will discuss the development of SOGI-HE through community-based research principles and the implications it has on future 2SLGBTQIA+ research conducted in the province of Manitoba (Canada). Further, we will discuss the ethical implications associated with queer data sovereignty in a precarious political climate for 2SLGBTQIA+ people and their rights.
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 | 0.023 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.022 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".