The Unintended Consequences of Information Provision: The World Health Organization and Border Restrictions during COVID-19
Bibliographic record
Abstract
Why do some international agreements fail to achieve their goals? Rather than states' engaging in cheap talk, evasion, or shallow commitments, the World Health Organization's (WHO) International Health Regulations (IHR)-the agreement governing states' and WHO's response to global health emergencies-point to the unintended consequences of information provision. The IHR have a dual goal of providing public health protection from health threats while minimizing unnecessary interference in international traffic. As such, during major outbreaks WHO provides information about spread and severity, as well as guidance about how states should respond, primarily regarding border policies. During COVID-19, border restrictions such as entry restrictions, flight suspensions, and border closures have been commonplace even though WHO recommended against such policies when it declared the outbreak a public health emergency in January 2020. Building on findings from the 2014 Ebola outbreak, we argue that without raising the cost of disregarding (or the benefits of following) recommendations against border restrictions, information from WHO about outbreak spread and severity leads states to impose border restrictions inconsistent with WHO's guidance. Using new data from COVID-19, we show that WHO's public health emergency declaration and pandemic announcement are associated with increases in the number of states imposing border restrictions.
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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 | 0.016 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".