Narratives of the SARS Epidemic and Ethical Implications for Public Health Crises
Bibliographic record
Abstract
The SARS (Severe Acute Respiratory Syndrome) epidemic provides a good case for study of crisis communication and the narratives used to respond to the epidemic. Interspecies transmission of a virus led to crisis in many public health networks, countries, and organizations. During this epidemic, competing narratives emerged, and were at odds with one another resulting in confusion, misinformation, and contagion of an often fatal disease. The narrative emerging in China led to the incomplete enactment of the broader, more global one that ultimately dominated organized global public health response. This case warrants close study because it is comprised of dimensions of organizational crisis, public health crisis, ethical crisis, and natural crisis in the origin of the disease. Lessons learned from the crisis response to the SARS epidemic include the need to respond with rapid, factual, and honest narratives and an ethical dedication to communicate on behalf of the public interest to prevent the needless spread of disease and loss of life.
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 | 0.019 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.029 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| 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".