Longcuts in the global migration network
Bibliographic record
Abstract
\n\nDifferent from the regular migration some migrants want the opportunity to move further to a new country rather than settle down. However, this peculiar “transit migration” is only studied based on small-scale surveys, which is regional and always controversial. To reconcile the contradiction of the fact that systematic issues such as immigration analysis need global large-scale data, this paper constructs a multilateral migration network and studies its statistical characteristics to get the probability of being transit countries and routes. It is quantified by irregular triangle relationships in the global migration network, and shows some popular springboards mentioned in prior research studies, such as Canada and Australia acting as the global hubs, and some typical transit countries directing to Europe, as Russia, Turkey, France and Germany. Besides, it also reveals and quantifies several hidden possible transit stations that were seldom noticed before, like South Africa, Israel and some hubs of local refugee flows in Africa. Exploring these possible routes and key notes might shed light on policy development, and from the viewpoint of physics, these results provide an objective view to transit migrants and countries that is free of prejudice and political attitudes.\n
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".