A High-Risk Body for Whom? On Fat, Risk, Recognition and Reclamation in Restorying Reproductive Care through Digital Storytelling
Bibliographic record
Abstract
This paper explores issues of weight stigma in fertility, reproduction, pregnancy and parenting through a fat reproductive justice lens. We engage with multimedia/digital stories co-written and co-produced with participants involved in Reproducing Stigma: Obesity and Women’s Experiences of Reproductive Care. This mixed methods research project which took place between 2015-2018 used interview and video-making methods with women-identified and trans people, as well as interviews with healthcare providers and policymakers to investigate perceptions and operations of weight and other stigma in fertility and pregnancy care. We consider the ways in which reproductive risk is typically storied in healthcare and culture, and analyse multimedia/digital stories made by participant-video-makers which story reproductive wellbeing differently. We examine three major themes—on risk, on recognition of weight and other stigma, and on reclamation of bodies—that emerged as critical to these storytellers as they navigated fatphobia in reproductive care. We argue that just as healthcare practitioners strive to practice evidence-based care we must also put into practice storied care—to believe, respect and honour fat people’s stories of their bodies and lives as fundamental to achieving equity and justice in reproductive healthcare.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.031 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".