The Global Gag Rule
Bibliographic record
Abstract
The Mexico City Policy, also known as the Global Gag Rule, is a U.S policy that requires foreign non-governmental organizations (NGOs) receiving U.S. global health funding to certify that they will not perform or actively promote abortion as a method of family planning. In 2017, President Donald Trump expanded the policy’s reach to include all global health assistance funding from U.S. agencies and departments. It is estimated that 1,275 foreign NGOs and US$8.8 billion in global health funding are subject to Trump’s expanded policy. Globally, an additional 2.2 million abortions, including 2.1 million unsafe abortions, are estimated to occur from 2017 to 2020 under President Trump’s administration. The Global Gag Rule undermines local sovereignty and jurisdiction over reproductive health law in countries that require these funds to operate and provide comprehensive reproductive services. This case study will highlight the effects of this policy by evaluating its quantitative and qualitative impacts and discussing the future implications for countries impacted by the policy. Results of this report demonstrate the policy’s failures in both achieving its own goals as well as international aims to improve global health and women’s rights among others.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".