“We Are the Woman and We Are the Man”: Insights from Focus Group Analysis on How Scholars Should Measure Sex and Gender
Bibliographic record
Abstract
Traditional ways of measuring sex and gender – often by conflating the two – have been criticized on empirical and normative grounds. This article presents the results of 15 focus groups conducted in Canada in 2016 that were motivated by two questions: How do people think about gender and how can this inform or refine current approaches to the measurement of gender? Several patterns emerged. First, participants seem to understand gender most often in the context of gendered roles and responsibilities, with physical appearances or traits playing secondary roles. Second, the salience of gender tends to be highest at home and in the workplace. Third, ethnicity/race, age, and sexual orientation are, predictably, important lenses through which individuals perceive and experience gender. Fourth, focus groups provide important opportunities for reflection on and improvement of gender measurement in survey research, including the different context-dependent factors that go into gender identity and the need for multi-dimensional measures. Taken together, findings help identify tangible practices that could be adopted for enhancing measurement of gender in survey research.
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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 | 0.074 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.032 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".