Measurements We Live By: Revisiting Sex and Gender in Canadian Sociology
Bibliographic record
Abstract
The project is guided by a theoretical question: how does Canadian sociology approach transgender and nonbinary populations? To understand how sociologists are engaging with gender in their research, a content analysis of publications in the Canadian Review of Sociology was performed. Data was collected for the conceptualization, measurement, and analysis of sex/gender for each of the publications from 2014 to 2024. A total of 109 research papers were included in the sample. Descriptive statistics were used to understand how researchers were conceptualizing, measuring, and analyzing sex and/or gender. Results show that 84.4% of publications (n=92) relied on a binary conceptualization of sex and/or gender. Regarding measurement, 90.8% of papers (n=99) relied on a binary tool for collecting data. 95.4% of the sample (n=104) analyzed sex/gender using a binary framework. Finally, results from logistic regression models show that year of publication was not able to significantly predict conceptualization, measurement, or analysis, meaning that there has been no statistically significant change in our methods over time. This project describes current methods used in published Canadian sociological research and supports the need for more open conversations about the role and use of sex and gender in our discipline.
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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 | 0.058 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.038 |
| Science and technology studies | 0.020 | 0.015 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".