Bibliographic record
Abstract
Positioning of the USA as the main global recipient of migrants and one of the largest centers of international tourism and passenger hubs, spontaneous nature of a large part of migration flows to America, predominance of people with global South background among foreign borne and their descendants, etc., objectively increase epidemic risks. These factors contributed to the penetration of COVID-19 into the country and its spread. The USA came out on top in the world in terms of the new virus morbidity and mortality and were struck by a deep and multifaceted coronacrisis. The experience of anti-epidemic control of people applying for entry into the United States was accumulated over the centuries-old history of immigration. Such experience fits into the tradition of restrictive immigration policies and has facilitated building a comprehensive whole-of-government system to combat the coronacrisis. The migration policy, based on the principle of “America First,” has become a concentrated expression of strategic tasks to ensure national security and social well-being of the population, woven together and embodied in measures to maintain sanitary and epidemiological safety and social protection of local workers under lockdown conditions. Bans on the entry into the United States of people who have previously visited countries with an unfavorable epidemic situation and restrictions on the movement of people across the borders of America with Mexico and Canada were introduced, as well as the suspension of the entry of several categories of immigrants and temporary foreign workers designed to slow down the spread of coronavirus and mitigate its negative socio-economic consequences for the local population. The epidemic has become a powerful catalyst for the long-term transformations of immigration policy that began with the coming to power of D. Trump, and the strengthening of its restrictive nature. At the same time, restrictive initiatives have affected international transport and tourism, are leading to a sharp contraction of migrant remittances to the countries of global South, hit the international refugee protection system. International relations of the United States are becoming more complicated, and the likelihood of a reorientation of some intellectual flows from the United States and dividends they bear to other world centers of migration gravity is increasing.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".