A Multilevel Governance Perspective on SARS in Toronto.
Bibliographic record
Abstract
Our current era is, among other things, characterized by its frequency and severity of epidemics\n(Francis, 2018). One of these epidemics was Severe Acute Respiratory Syndrome (SARS) in 2003.\nCanada was the most effected country outside of Asia, and Toronto was the site of North America’s\nlargest outbreak of SARS (Svoboda et al., 2004). SARS was declared a ‘worldwide health threat’ by the\nWHO (Hanrieder & Kreuder-Sonnen, 2014) and when SARS started to spread in Toronto, the Premier\ndeclared a provincial emergency (Committee, 2003). Hanrieder and Kreuder-Sonnen (2014) define this\ntype of governance during a period of securitization as ‘emergency governance’ and argue that using\nthis language of ‘global security’ facilitates the emergence of authoritative actors whose decision can\nshape how the threat is governed. The focus of this research is on emergency governance after the\naforementioned period of successful securitization. Securitization is most often linked to nation states,\nwhich can take emergency action against existential threats. However, this research argues that the\nsecuritization of international problems may lead to response mechanisms across all levels of\ngovernance, such as the WHO by announcing a worldwide health threat. In line with recent research\nthat emphasizes the multi-layered nature of international governance, this research moves beyond\nthe narrow focus on nation-states and includes international governmental bodies as well as local\nactors by adding a multilevel governance perspective (Schakel, 2016). This research will shed light on\nhow different actors, during different phases of the emergency governance of SARS in Toronto, shape\nmeasures and policy. This research will demonstrate that in the period between securitization and desecuritization it is not merely the securitizing actor that is using extraordinary measures to tackle the\nthreat, but that there is a constant interaction between the government and other actors in shaping\nmeasures and policy responses.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".