A virus unites the world while national border closures divide it: Epidemiologic, legal, and political analysis on border closures during COVID-19
Bibliographic record
Abstract
This article critically examines the use of national border closures at the outset of the COVID-19 pandemic. After explaining why targeted border closures generally do not work and how they violated international law at the time, we examine the unprecedented case of total border closures. Positing that since the current instruments and institutions of global health governance did not anticipate this phenomenon, the legality of total border closures rests on less certain grounds. Then, after asking why nearly every government implemented some form of border closure in March 2020 if neither science nor law provided adequate motivation for their use, we conclude that in the face of a global health emergency, border closures represent an opportunity for political leaders to show determined action, redirect blame to other jurisdictions, and reinforce nationalism. We proceed to argue that both targeted and total border closures have profound legal, epidemiological, and political significance as performances that contradict global realities while undermining notions of global solidarity. Such political theatre means that citizens must weigh these consequences against any perceived benefits of border closures as they would any other politically driven government action, and contest and challenge them appropriately. Citizens must not unduly defer to scientists or lawyers on early COVID-19 border closures because these were primarily political-not scientific or legal-decisions. In this vein, we conclude with some guiding political considerations for scrutinizing government decisions to close borders and observations for the future of global health cooperation during infectious disease outbreaks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".