Menstruation Beyond “Womanhood”: Understanding and Inscribing Queer Experiences of Menstruation in Montreal
Bibliographic record
Abstract
This thesis problematizes the assumption that menstruation is exclusively a woman's issue. Inspired by my own personal discomfort around menstruation, the question this research seeks to answer is “how do queer— more specifically, trans and non-binary—individuals in Montreal experience and talk about menstruation?” To address this question, I detail the menstrual experiences of three nonbinary individuals, one transgender man, and one transgender woman. I engage with Interpretative Phenomenological Analysis (IPA) to interpret their accounts. The method was developed by Jonathan A. Smith, Paul Flowers, and Michael Larkin (2009) to understand life experiences and embodied phenomena through a rigorous engagement with participants’ narratives. The method has been adapted for anthropological use in the current thesis and has been used in conjunction with a digital ethnography of Instagram and TikTok to analyze how menstruation is discussed on social media platforms. The most significant theme that emerged from data analysis is that neoliberal policies and bio-power influence how transgender and nonbinary people experience menstruation. Some participants experience the effects firsthand when trying to access medical care, while others feel it more discretely in the ways that menstrual products are marketed. This theme is explored at length in the thesis’ third chapter but runs as an undercurrent throughout the rest of the chapters. This thesis contributes to research on trans and nonbinary experiences of menstruation and aims to promote an understanding of menstruation outside of womanhood. I conclude that menstruation is a gender-neutral bodily function and should be understood as such.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".