National probabilities of the coronavirus spreading over time in Europe based on migration networks
Bibliographic record
Abstract
Global migration trends (Hatton−Williamson \n2005, Bálint et al 2017, Farkas–Dövényi 2018) \ntoday differ from those in previous centuries in \nterms of both the number of people migrating \n(as of 2017, 272 million people live in a country \nother than their country of origin) as well as the \ngeographical, economic, and cultural distance \nbetween sending regions and destination \ncountries. The interconnection between \ncountries is constantly growing, relationships are \nexpanding through migration, and people's \nmovement is increasing. \nMigration shows strong territorial concentration \n(Winders 2014); in 2019, half of the global \nmigrant population lived in nine countries. In \ninternational migration, there are centres (large \nhost countries) and global migration destinations, \nwhich attract migrants over long distances. Such \nhubs include the USA, Canada, Australia, the \nUK, Germany, France and Spain. \nClose migration relationships mean strong \nexposure and vulnerability to the spread of \ninfectious diseases. The calculation does not \nassume that infection can only be caused by \nmigration, but states that migration relationships \nbetween countries, that is, their network, well \nrepresent the spread of infections between \ncountries.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".