Fat Reproductive Justice: Navigating the Boundaries of Reproductive Health Care
Bibliographic record
Abstract
Abstract In this paper, we explored the experiences of people in larger bodies seeking fertility and/or pregnancy care through a reproductive justice lens, integrating an understanding of weight stigma with an understanding of who has access to reproductive technologies, who is “allowed” to become pregnant, and the discourses that surround pregnancy. We conducted a thematic analysis of the narratives of 17 participants who had been labeled “overweight” or “obese” while pregnant and/or seeking reproductive health care related to fertility and/or pregnancy. Participants’ narratives speak to experiences of being surveilled and controlled in medical settings; this surveillance and control negatively impacted their access to desired care. In order to receive the kinds of care they wanted, many participants had to become self‐advocates. This self‐advocacy speaks to resistance and “resilience”; we discuss how individualizing “resilience” represents an incomplete solution to navigating the shaming and blaming encounters participants experienced with healthcare providers. We argue for health care that is more caring and responsive to the needs of diverse individuals who are or who are seeking to become pregnant.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.058 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.007 |
| 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".