“How might this have gone differently if I was not Black?”: Black Mothers Navigating Perinatal Care in the Greater Toronto Area
Bibliographic record
Abstract
Despite documented histories of medical violence against Black women—including harmful interventions, exploitative research, and systemic mistreatment—and the persistent pathologization of Black women in popular narratives of motherhood, there remains a paucity of research on the perinatal experiences of Black women in Canada. This article examines the shared yet distinct perinatal experiences of Black women, drawing on birth stories from the Greater Toronto Area of women who sought care between January 2020 and May 2023. Through their birth stories, we explore the reproduction of race by medical staff, focusing on the stereotype of Black women as inherently resilient and the racial contempt they encounter in perinatal care. Birth stories reveal key strategies that Black women are forced to use to resist obstetric racism and the pathologization of Black motherhood, including prioritizing clear communication, seeking representation among healthcare providers, and choosing midwives as primary caregivers. These experiences provide valuable insights for shaping healthcare policies that centre Black women and birthing people while advancing reproductive justice and dismantling medical violence.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".