Gender, Fat, and “Reproductive” Health Care
Bibliographic record
Abstract
Inspired by reports of BMI cut-offs for life-saving ventilators during the COVID-19 pandemic, this chapter explores the ways in which a “logics of eugenics” is deployed on fat bodies through reproductive care practices. The authors draw on literatures of obesity stigma and fat eugenics and report on results of qualitative research conducted in Winnipeg, Canada, with 25 self-identified fat or “obese” women exploring their experiences of health care during conception, pregnancy, and birth. The research was part of a larger cross-Canada study of 59 participants titled Reproducing Stigma . Overwhelmingly, participants described narratives of discouragement and practices of reluctant care targeted at their bodies by reproductive health care professionals who emphasized health risks to the foetus associated with “maternal obesity,” and shamed participants both for their weights and desire to mother. Participant stories demonstrate, the authors argue, the diffuse and complicated ways in which eugenics is unintentionally practiced on fat bodies within medical practice through discourses of risk that articulate fat bodies as unhealthy vectors of reproduction. The authors thus contribute to a budding scholarship in the area of obesity stigma, demonstrating how the active discouragement and curtailment of fat women’s reproduction suggests that they may face a very specific and significant type of stigma based on body size, connecting to a long history of eugenic population control in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".