The Unintended Consequences of Information Provision: The World Health Organization and Border Restrictions during COVID-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it