Health and foreign policy: influences of migration and population mobility
Bibliographic record
Abstract
International interest in the relationship between globalization and health is growing, and this relationship is increasingly figuring in foreign policy discussions. Although many globalizing processes are known to affect health, migration stands out as an integral part of globalization, and links between migration and health are well documented. Numerous historical interconnections exist between population mobility and global public health, but since the 1990s new attention to emerging and re-emerging infectious diseases has promoted discussion of this topic. The containment of global disease threats is a major concern, and significant international efforts have received funding to fight infectious diseases such as malaria, tuberculosis and HIV/AIDS (human immunodeficiency virus/acquired immune deficiency syndrome). Migration and population mobility play a role in each of these public health challenges. The growing interest in population mobility's health-related influences is giving rise to new foreign policy initiatives to address the international determinants of health within the context of migration. As a result, meeting health challenges through international cooperation and collaboration has now become an important foreign policy component in many countries. However, although some national and regional projects address health and migration, an integrated and globally focused approach is lacking. As migration and population mobility are increasingly important determinants of health, these issues will require greater policy attention at the multilateral level.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".