Binary blues: Exploring beyond dichotomized gender comparisons with a theory-driven approach
Bibliographic record
Abstract
The call to move beyond binary conceptualizations of gender is not new, and yet, this categorical and contrastive approach to gender analysis remains common, particularly in health sciences. It has been posited that the problem of gender dualism rests partially in the minimal interplay between theory and method. Drawing on our experiences during a qualitative study of men’s and women’s involvement in cardiac rehabilitation, this article provides an account of the analytic and reflexive challenges of conducting research on gender and health and explores how the careful use of theory, specifically Bourdieu’s theory of practice, can facilitate a departure from narrow gender binaries. The analysis presented in this article adds to methodological writings on gender and health, offering a theory-driven process to help researchers address the fluidity of gender as lived and negotiated in the everyday social and material circumstances of men and women, particularly during times of illness.
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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 | 0.061 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.044 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".