Measuring the Growth of Gender-Inclusive Surveys Around the World
Bibliographic record
Abstract
As ideas about gender identity evolve, survey researchers around the world are working to understand how best to measure sex and gender in a way that is both accurate and inclusive. Emerging best practices differ widely between countries based on cultural and societal norms and the construction of language around gender. In this paper, we examine how survey creators have changed how they ask about gender in the past decade across 11 linguistically and culturally diverse countries. We measure the number of answer options included in gender questions created by SurveyMonkey users between 2012 and 2022. Our findings show that the number of gender questions with more than two answer options increased in all countries examined in our research. Canada, the United Kingdom, and Australia show the highest levels of gender questions with more than two answer options in 2022, while Egypt and Nigeria have the lowest levels.
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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 | 0.139 | 0.305 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".