Bibliographic record
Abstract
“Risk communications” has acquired some importance in the wake of our experience of SARS. Handled well, it helps to build mutual respect between a government or an organisation and the target groups with which it is communicating. It helps nurture public trust and confidence in getting over the crisis. The World Health Organization (WHO) has also come to recognise its importance after SARS and organised the first Expert Consultation on Outbreak Communications conference in Singapore in September 2004. This article assesses the context and the key features which worked to Singapore’s advantage. Looking at the data now widely available on the Internet of the experience of SARS-infected countries like China, Taiwan, Canada, the article identifies the key areas of strategic communications in which Singapore fared particularly well. Another issue discussed is whether Singapore’s experience has universal applicability or whether it is limited because of Singapore’s unique cultural, historical and geographical circumstances. Finally, the article also looks at some of the post-SARS enhancements that have been put in place following the lessons learnt from SARS and the need to confront new infectious outbreaks like avian flu. Key words: Confidence building, Risk, Technological aids, Transparency, Trust
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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".