The Role of Health Researchers in Documenting Health Suffering and Crimes against Humanity Resulting from 2018 US Sanctions against Iran
Bibliographic record
Abstract
On November 20, 2018, the United States imposed unilateral sanctions on the Republic of Iran. The intention of these sanctions, which are being used in conjunction with other political pressures, is to impose financial hardship on Iran for its perceived support of Syrian president Bashar al-Assad and terrorism. The consequences of these sanctions for the Iranian population will be manifold, with health likely to be one of the first sectors to suffer. There is no designated international body responsible for monitoring population health in the wake of sanctions; thus, health researchers have a pivotal role to play in the international community. The timely collection of health data can supply bodies such as the United Nations Security Council with information about the justness of the US sanctions and can be used in making arguments to protect human rights, including health, and in preventing crimes against humanity. This article briefly explains the concept of crimes against humanity and how health data and health service researchers can play an important role in drawing attention to declining health indicators in a sanctioned country.
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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 | 0.058 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".