The Case for Non-Binary Gender Questions in Surveys
Bibliographic record
Abstract
ABSTRACT LGBTQ activists and academics advocate the use of non-binary gender categories to include individuals who identify as neither rigidly male nor rigidly female to reflect the increasing number of people who do not place themselves in these two conventional classes. Although some general-population surveys have begun using non-binary gender questions, research has not examined the consequences of using (or not) a question with non-binary gender categories in surveys and censuses. Our study addresses this gap using a survey experiment in which respondents in the United States, Canada, and Sweden randomly received a binary or a non-binary gender question. We find no evidence of negative reactions to the non-binary question. Moreover, when there is a statistical difference, the reactions are positive. We thus conclude that general-population surveys could use a non-binary question without facing significant adverse reactions from respondents.
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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 | 0.582 | 0.765 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".