The Politics of SARS – Rational Responses or Ambiguity, Symbols and Chaos?
Bibliographic record
Abstract
The main focus in this article is on the SARS event as a political process, involving political leaders, administrators and health professionals. How can we understand the reactions to SARS of some of the main actors and institutions? What aspects were they preoccupied with and did their definition of what SARS was all about change during the process? A selection of jurisdictions is chosen - China, the Hong Kong SAR, Canada and the World Health Organization - to explore these questions. The starting point is a view that the reactions cannot primarily be seen as an instrumental, based on rational, standard-operating-procedures (SOPs) and technical expertise, but may be better understood by a garbage can-perspective. From a review of the events as publicly reported, we find, as suggested by garbage can theory, that politicians' and administrators' responses to the SARS outbreak were a combination of competing rationalities and overlapping agendas. Critical decisions were triggered by extraneous factors and administrative actions were shaped by dramatic switches from one set of standard operating procedures to another, as events unfolded. The public health issues constantly vied with other agendas and only when compelling alignments among them occurred did professional or technical rationales for "solving the problem" become dominant.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.060 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".