US Land Borders under Conditions of COVID-19 Pandemic
Bibliographic record
Abstract
The analysis of the functioning of the US land border in a pandemic showed that despite the changing health situation in Canada and Mexico, the US did not adapt the restrictions on land borders, which led to a number of negative socio-economic consequences for all countries. The lack of transparency on the part of US federal officials indicates a lack of a clear plan of action to address this pressing issue. The United States continues a strict set of restrictions that prohibit the opening of borders. The closure of the border at the state level has harmed companies that rely on tourists to generate income, as well as socio-economic losses for border communities. It is stated that in the USA there is no clear plan for the decision of problems of frontier in the conditions of pandemic COVID-19. In general, the federal plan to restore the US border will depend on positive health conditions, such as a low number of active cases and a high level of vaccination. Although the emergence of new highly contagious strains makes it unlikely that the border will return to normal state before the pandemic in the near future. In this context, maintaining existing restrictions without a public plan to ease them has a political cost. Taking into consideration election campaigns in Mexico and Canada in 2021 leaders' mistakes in addressing the economic and social losses caused by the pandemic policy at the border are a political burden. Therefore, risk management is a smart strategy for both politicians and border security. As U.S. officials continue to engage in the country's border policy in emergencies, transparent consideration of the various economic, social, and medical situations in Canada and Mexico should become a top socioeconomic priority
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".