A Research Note on Canada's LGBT Data Landscape: Where We Are and What the Future Holds
Bibliographic record
Abstract
There is a growing international literature on the lives of lesbian, gay, bisexual, and transgender (LGBT) individuals. One of the biggest limitations for researchers in this field continues to be the dearth of population-based surveys that include questions on sexual orientation, gender identity, and high-quality demographic, health, social, political, or economic variables. This research note provides an overview of the current LGBT data landscape in Canada. We start with some of the challenges for researchers studying the LGBT community, including issues of sample size, measurement, response bias, and concealment. Next, we provide an overview of Canadian surveys that include questions on sexual orientation and/or gender identity, including the strengths and weaknesses of each. We end with a brief discussion on newly available administrative data and provide recommendations for researchers and policymakers moving forward.
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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 | 0.035 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.025 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".