Toward Global Comparability of Sexual Orientation Data in Official Statistics: A Conceptual Framework of Sexual Orientation for Health Data Collection in New Zealand’s Official Statistics System
Bibliographic record
Abstract
OBJECTIVE: Effectively addressing health disparities experienced by sexual minority populations requires high-quality official data on sexual orientation. We developed a conceptual framework of sexual orientation to improve the quality of sexual orientation data in New Zealand's Official Statistics System. METHODS: We reviewed conceptual and methodological literature, culminating in a draft framework. To improve the framework, we held focus groups and key-informant interviews with sexual minority stakeholders and producers and consumers of official statistics. An advisory board of experts provided additional guidance. RESULTS: The framework proposes working definitions of the sexual orientation topic and measurement concepts, describes dimensions of the measurement concepts, discusses variables framing the measurement concepts, and outlines conceptual grey areas. CONCLUSION: The framework proposes standard definitions and concepts for the collection of official sexual orientation data in New Zealand. It presents a model for producers of official statistics in other countries, who wish to improve the quality of health data on their citizens.
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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 | 0.457 | 0.439 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.027 | 0.027 |
| Science and technology studies | 0.008 | 0.050 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".