The Canadian general public's views of gender and mathematics: a comparison of findings from binary and non-binary studies
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we discuss findings from a portion of a larger study, in which members of the Australian and Canadian general public were queried about their views of gender and mathematics. Specifically – and novel to the field – the questionnaire that was used to collect the data was completely non-binary in nature (e.g., “For which gender…”). Here, we focus on the Canadian dataset, and draw comparisons to findings from an earlier study [8], in which the general public were asked about their views of gender and mathematics using a similar questionnaire, but with binary (e.g., “girls or boys”) wording. We discuss differences in the findings of the two studies and consider how the wording of the questionnaires may have contributed to the differences in findings. We conclude by providing suggestions for conducting “gender issues” research in mathematics education in ways that reflect contemporary perspectives of gender.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it