International Border Restrictions During COVID-19 as Global Health Security Theatre
Bibliographic record
Abstract
Abstract During outbreaks of diseases like cholera, HIV/AIDS, H1N1, and Ebola, governments often impose international border restrictions (for example, quarantines, entry restrictions, and import restrictions) that disrupt the economy without stopping the spread of disease. During COVID-19, international travel restrictions were ubiquitous despite initial World Health Organization recommendations against such measures because of their limited public health benefit and the potential for imposing a range of harms. Why did governments adopt these measures? This article argues and finds evidence that governments use international border restrictions as security theatre: ‘measures that provide not security, but a sense of it’. Quantitative analysis of original data on states’ first border restrictions during the pandemic suggests that behaviour was not just driven by the risk of COVID-19 spread. Instead, nationalist governments, which are likely to be attracted to policies associating disease with foreigners, were more likely to impose border restrictions, did so more quickly, and adopted domestic measures more slowly. A case study of the US further illustrates the security theatre logic. The findings imply that overcoming or redirecting governments’ attraction to security theatre could promote international cooperation during global health emergencies.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".