Effects of Including Gender Pronoun Questions in Surveys
Bibliographic record
Abstract
This research examines whether the mere presence of asking about gender pronouns (e.g., she/her, he/him, they/them, and ze/zir) in a survey enhances participants’ attitudes and satisfaction of answering the questions. A large sample (N = 1,511) of heterosexual, cisgender, and LGBTQIA+ participants across the United States (US) were surveyed an online “personality test” (as a deception), with the real purpose of examining whether asking a pronoun question enhanced their perceptions of the survey. Three demographic groups were included: (i) heterosexual–cisgender (n = 503), (ii) gay–cisgender (n = 509), and (iii) genderqueer (trans, non-conforming, other, n = 499). Half of each group were randomly given either a survey that included a gender pronoun question (test) or not (control), and then all rated their perceptions of the survey questions. For participants who identified as heterosexual or gay, no major differences were found between survey conditions. However, participants who identified as genderqueer experienced significant increases of satisfaction, comfort level, and perceived relevance of the questions when given a survey that asked their gender pronouns versus the survey that did not. These findings have implications for any surveys that ask about personal demographics, and suggest that any form of written communication should include clarity about gender pronouns.
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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 | 0.091 | 0.404 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".