Human Rights and Maternal Health: Exploring the Effectiveness of the <i>Alyne</i> Decision
Bibliographic record
Abstract
This article explores the effectiveness of the decision of the Committee on the Elimination of Discrimination against Women in the case of Alyne da Silva Pimentel Teixeira (deceased) v. Brazil, concerning a poor, Afro-Brazilian woman. This is the first decision of an international human rights treaty body to hold a state accountable for its failure to prevent an avoidable death in childbirth. Assessing the future effectiveness of this decision might be undertaken concretely by determining the degree of Brazil's actual compliance with the Committee's recommendations, and how this decision influences pending domestic litigation arising from the maternal death. Alternative approaches include: determining whether, over time, the decision leads to the elimination of discrimination against women of poor, minority racial status in the health sector, and if it narrows the wide gap between rates of maternal mortality of poor, Afro-Brazilian women and the country's general female population. Determining the effectiveness of this decision will guide whether to pursue a more general strategy of judicializing maternal mortality.
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 | 0.086 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.055 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".