Human Mobility and Population Health: New Approaches in a Globalizing World
Bibliographic record
Abstract
The globalization of economies in the last 25 years has greatly increased both the number of people on the move and the rapidity of their movement, and has brought attention to global disparities in health determinants and to the health of migrant populations themselves. Differences in epidemiological disease risk (prevalence gaps) may have negative, neutral, or positive health consequences for the migrant or receiving population. Population mobility represents a growing challenge to the development of public health programs and legislative policies to prevent the importation of disease, and to promote and protect the health of migrants and the local, receiving population. The inability to detect and contain imported disease threats at national borders requires a shift in immigration, quarantine, and public health approaches to health and mobile populations. A new paradigm is needed to facilitate the development of policies and programs to address the health consequences of population mobility.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.042 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".