Gender Polarization and Sociodemographic Axes in Canada
Bibliographic record
Abstract
Although quantitative social scientists have examined how self-perceptions of masculinity and femininity vary by sociodemographic axes, gender polarization has rarely been studied. This concept captures gender subjectivities in ways that reflect how many individuals understand themselves as having a mixture of masculine and feminine characteristics, helping align survey measures with how gender theorists and qualitative scholars have examined gender. It is measured as the absolute value of the difference between self-rated masculinity and femininity. For example, if someone rated themselves as a 4 of 7 on masculinity and a 7 of 7 on femininity, their gender polarization value would be 3. In contrast, if someone rated themselves as a 1 of 7 on masculinity (the lowest value) and a 7 of 7 on femininity, their value would be 6. The authors examine this metric in a probability sample of Canadians. Gender polarization differed by political ideology, sexual identity, and age cohort for both women and men. For men only, race/ethnicity, educational attainment, and rural or urban location were related to gender polarization. These results highlight how social and structural contexts both shape and constrain how individuals perceive themselves in gendered 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".