Nego-feminism as a strategy to improve access to abortion in sub-saharan Africa
Bibliographic record
Abstract
BACKGROUND: Abortion is partially legal in 48 of 54 countries in Sub-Saharan Africa (SSA); however, abortion laws are generally weakly implemented, and evidence suggests that extending abortion rights does not necessarily improve abortion access. OBJECTIVE: Reflecting on the implementation challenges faced by the laws extending rights to abortion in SSA, and enriching this approach by considering complementary avenues to overcome barriers in access to abortion. ARGUMENT: Reproductive justice is a theory that emphasizes the importance of contexts and different levels of societal forces in shaping reproductive freedom. From a reproductive justice perspective, we suggest that the successful implementation of abortion laws is hampered by discrepancies between legal frameworks and socio-cultural contexts in many SSA countries. In many SSA contexts, the legalization of abortion has not been accompanied by a modification of socio-cultural contexts regarding abortion. Until these contexts are more receptive to abortion, implementation issues may persist and access to abortion may remain hindered. Since increasing social acceptability of abortion can be a lengthy process, exploring complementary strategies to improve abortion access can be beneficial. Nego-feminism, an African feminist theory rooted in African values of negotiation and relationships, may be an effective strategy to navigate societal forces to improve abortion access, in the meantime, until greater acceptability and enforcement of abortion laws. An illustration of this promising strategy can be found in abortion accompaniment models such as MAMA network which provide safe access to medication abortion in the informal sector. CONCLUSION: Nego-feminism could potentially improve access to abortion in legally and socially restricted settings. However, the continued fight for the legalization of abortion is essential, while using nego-feminism as a complement.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".