Domestication of the Maputo Protocol in the Democratic Republic of Congo: Leveraging regional human rights commitments for abortion decriminalization and access
Bibliographic record
Abstract
The Maputo Protocol, adopted over 20 years ago, is a promising regional treaty for advancing gender equity and sexual and reproductive health and rights. This instrument has driven progress in women's health and rights across Africa, with much remaining to achieve to realize its full potential for women and girls, including access to safe abortion. The present paper shares the strategies and lessons from the Democratic Republic of Congo's (DRC) reform centered on the domestication of the Protocol, specifically applying its commitments on abortion decriminalization and access. With a vision of addressing maternal mortality and rectifying the impacts of widespread sexual violence against women during war, abortion as a human right and health imperative was at the heart of the DRC's reform. Governmental commitment, broad coalition building, evidence generation, and an intersectional advocacy agenda were critical to overcoming opposition, stigma, and other challenges. This paper shares key learnings from the DRC's complex yet collaborative reform strategies and its processes. The strategy prioritized domestication of the Protocol for numerous reforms, including paving the path to legal abortion on the broad grounds of rape or incest, and saving women's health and/or life. With a commitment to maximizing quality, access, task sharing, and equity, progressive national comprehensive abortion guidelines were created alongside an implementation roadmap for accountability. The DRC's experience leveraging the Maputo Protocol's obligations to advance abortion rights and access offers valuable insights for consideration globally.
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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 | 0.037 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".