“Deep questions for a Saturday morning”: An investigation of the Australian and Canadian general public's definitions of gender
Bibliographic record
Abstract
Abstract Objective Many studies exist about people's views of gender in a wide variety of fields. However, participants are not typically asked what they think gender means; rather, gender is presumed to have a taken‐as‐shared meaning. Methods As part of a larger study conducted in Australia and Canada about the general public's views of gender and mathematics, we investigated participants’ definitions of the term gender. We considered overall trends and trends by demographic group (country, gender, age, and education level). Results Most commonly, gender was defined as a person's feelings or self‐identification. Participants also frequently solely used the terms male and female or discussed biological features. However, response patterns varied widely by demographic group. Conclusion Due to these diverse and sometimes contradictory definitions, we argue that researchers cannot assume that participants have common understanding of the term gender. We conclude by providing suggestions for how gender‐focused research can be done in more transparent ways.
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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 | 0.025 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.024 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".