Narratives of Essentialism and Exceptionalism: The Challenges and Possibilities of Using Human Rights to Improve Access to Safe Abortion.
Bibliographic record
Abstract
As this special section of Health and Human Rights goes to press, women’s access to sexual and reproductive health, including safe and legal abortion, faces both old and new threats in many corners of the world. Among other things, the US government under Donald Trump decided to defund the United Nations Population Fund and to reinstate and expand the so-called Global Gag Rule that prevents any non-US, nongovernmental organization from receiving funds from the United States if they provide not just abortion services but any information regarding abortion, even with other donors’ funds. USAID is the largest donor in the world for family planning services, and grantees will lose funding unless they agree to these conditions. As many as 50 European and other governments, including Canada, stepped in to try to make up at least in part for the loss in funding. Now that it has been announced that all US global health assistance funding for international health programs, such as for HIV/AIDS, maternal and child health, malaria, global health security, and family planning and reproductive health will be affected, the losses may be as much as US$9 billion.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".