Best Evidence Aside: Why Trump's Executive Order Makes America Less Healthy
Bibliographic record
Abstract
Although the immigration and nationality act gives the President power to suspend entry of classes of aliens to the US, he cannot discriminate on grounds of nationality or religion. The constitutional arguments based on religious freedom, establishment of religion, and equal protection appear powerful from a moral perspective, but face legal hurdles because the Order on its face does not discriminate against Muslims. According to UNICEF, four of the countries targeted – Syria, Yemen, Sudan and Somalia – rank among the world’s most hazardous for children’s health and dignity. In Iraq alone, >5 million children are in peril, with one-quarter displaced from their homes by conflict. Refugees often spend years in living conditions that exacerbate injury and disease: crowded and unsanitary spaces fan the spread of infectious diseases (cholera and tuberculosis); refugees risk sexual assaults, which may lead to sexually transmitted infections, as well as mental trauma; and they lack access to preventative services, as well as basic health care, including safe childbirth. President Trump’s order denied entry of improbable threats—a 9 year-old Somali child with congenital heart disease and a 1 year-old Sudanese boy with cancer—both seeking medical treatment. Within days of the first executive order, a terrorist entered a Quebec mosque and murdered six people, injuring eighteen others. This atrocity underscores a sad truth—most victims of Islamic-inspired terrorism are Muslims, and most attackers are home grown. The President is rapidly eroding two of America’s greatest values—inclusiveness and diversity, endangering America’s position as a liberal beacon of freedom 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 | 0.022 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.039 | 0.008 |
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".