The human rights impact of gender stereotyping in the context of reproductive health care
Bibliographic record
Abstract
Gender stereotypes surrounding women's reproductive health impede women's access to essential reproductive healthcare and contribute to inequality more generally. Stereotyping in healthcare settings impedes women's access to contraceptive information, services, and induced abortion, and lead to involuntary interventions in the context of sterilization. Decisions by human rights monitoring bodies, such as the Inter-American Court of Human Rights' case, IV v. Bolivia, which was a case concerned with the involuntary sterilization of a woman during childbirth, highlight how stereotypes in the context of providing health care can operate to strip women of their agency and decision-making authority, deny them their right to informed consent, reinforce gender hierarchies and violate their reproductive rights. In the present article, IV v. Bolivia is examined as a case study with the objective being to highlight how, in the context of coercive sterilization, human rights law has been used to advance legal and ethical guidelines, including the International Federation of Gynecology and Obstetrics' (FIGO) own guidelines, on gender stereotyping and reproductive healthcare. The Inter-American Court's judgment in IV v. Bolivia illustrates the important role FIGO's guidance can play in shaping human rights standards and provides guidance on the service provider's role and responsibility in eliminating gender stereotypes and upholding and fulfilling human rights.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.047 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".