Trust, risk, and the challenge of information sharing during a health emergency
Bibliographic record
Abstract
Information sharing is a critical element of an effective response to infectious disease outbreaks. The international system of coordination established through the World Health Organization via the International Health Regulations largely relies on governments to communicate timely and accurate information about health risk during an outbreak. This information supports WHO's decision making process for declaring a public health emergency of international concern. It also aides the WHO to work with governments to coordinate efforts to contain cross-border outbreaks.Given the importance of information sharing by governments, it is not surprising that governments that withhold or delay sharing information about outbreaks within their borders are often condemned by the international community for non-compliance with the International Health Regulations. The barriers to rapid and transparent information sharing are numerous. While governments must be held accountable for delaying or withholding information, in many cases non-compliance may be a rational response to real and perceived risks rather than a problem of technical incapacity or a lack of political commitment. Improving adherence to the International Health Regulations will require a long-term process to build trust that incorporates recognizing and mitigating the potential and perceived risks of information sharing.
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 | 0.063 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".